spaCy/website/docs/api/language.jade

//- 💫 DOCS > API > LANGUAGE

include ../../_includes/_mixins

p
    |  A text-processing pipeline. Usually you'll load this once per process,
    |  and pass the instance around your application.

+h(2, "init") Language.__init__
    +tag method

p Initialise a #[code Language] object.

+aside-code("Example").
    from spacy.language import Language
    nlp = Language(pipeline=['token_vectors', 'tags',
                             'dependencies'])

    from spacy.lang.en import English
    nlp = English()

+table(["Name", "Type", "Description"])
    +row
        +cell #[code vocab]
        +cell #[code Vocab]
        +cell
            |  A #[code Vocab] object. If #[code True], a vocab is created via
            |  #[code Language.Defaults.create_vocab].

    +row
        +cell #[code make_doc]
        +cell function
        +cell
            |  A function that takes text and returns a #[code Doc] object.
            |  Usually a #[code Tokenizer].

    +row
        +cell #[code pipeline]
        +cell list
        +cell
            |  A list of annotation processes or IDs of annotation, processes,
            |  e.g. a #[code Tagger] object, or #[code 'tagger']. IDs are looked
            |  up in #[code Language.Defaults.factories].

    +row
        +cell #[code meta]
        +cell dict
        +cell
            |  Custom meta data for the #[code Language] class. Is written to by
            |  models to add model meta data.

    +footrow
        +cell return
        +cell #[code Language]
        +cell The newly constructed object.

+h(2, "call") Language.__call__
    +tag method

p
    |  Apply the pipeline to some text. The text can span multiple sentences,
    |  and can contain arbtrary whitespace. Alignment into the original string
    |  is preserved.

+aside-code("Example").
    tokens = nlp('An example sentence. Another example sentence.')
    tokens[0].text, tokens[0].head.tag_
    # ('An', 'NN')

+table(["Name", "Type", "Description"])
    +row
        +cell #[code text]
        +cell unicode
        +cell The text to be processed.

    +row
        +cell #[code **disabled]
        +cell -
        +cell Elements of the pipeline that should not be run.

    +footrow
        +cell return
        +cell #[code Doc]
        +cell A container for accessing the annotations.

+h(2, "update") Language.update
    +tag method

p Update the models in the pipeline.

+aside-code("Example").
    with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
        for epoch in trainer.epochs(gold):
            for docs, golds in epoch:
                state = nlp.update(docs, golds, sgd=optimizer)

+table(["Name", "Type", "Description"])
    +row
        +cell #[code docs]
        +cell iterable
        +cell A batch of #[code Doc] objects.

    +row
        +cell #[code golds]
        +cell iterable
        +cell A batch of #[code GoldParse] objects.

    +row
        +cell #[code drop]
        +cell float
        +cell The dropout rate.

    +row
        +cell #[code sgd]
        +cell function
        +cell An optimizer.

    +footrow
        +cell return
        +cell dict
        +cell Results from the update.

+h(2, "begin_training") Language.begin_training
    +tag contextmanager

p
    |  Allocate models, pre-process training data and acquire a trainer and
    |  optimizer. Used as a contextmanager.

+aside-code("Example").
    with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
        for epoch in trainer.epochs(gold):
            for docs, golds in epoch:
                state = nlp.update(docs, golds, sgd=optimizer)

+table(["Name", "Type", "Description"])
    +row
        +cell #[code gold_tuples]
        +cell iterable
        +cell Gold-standard training data.

    +row
        +cell #[code **cfg]
        +cell -
        +cell Config parameters.

    +footrow
        +cell yield
        +cell tuple
        +cell A trainer and an optimizer.

+h(2, "use_params") Language.use_params
    +tag contextmanager
    +tag method

p
    |  Replace weights of models in the pipeline with those provided in the
    |  params dictionary. Can be used as a contextmanager, in which case, models
    |  go back to their original weights after the block.

+aside-code("Example").
    with nlp.use_params(optimizer.averages):
        nlp.to_disk('/tmp/checkpoint')

+table(["Name", "Type", "Description"])
    +row
        +cell #[code params]
        +cell dict
        +cell A dictionary of parameters keyed by model ID.

    +row
        +cell #[code **cfg]
        +cell -
        +cell Config parameters.

+h(2, "pipe") Language.pipe
    +tag method

p
    |  Process texts as a stream, and yield #[code Doc] objects in order.
    |  Supports GIL-free multi-threading.

+aside-code("Example").
    texts = [u'One document.', u'...', u'Lots of documents']
    for doc in nlp.pipe(texts, batch_size=50, n_threads=4):
        assert doc.is_parsed

+table(["Name", "Type", "Description"])
    +row
        +cell #[code texts]
        +cell -
        +cell A sequence of unicode objects.

    +row
        +cell #[code n_threads]
        +cell int
        +cell
            |  The number of worker threads to use. If #[code -1], OpenMP will
            |  decide how many to use at run time. Default is #[code 2].

    +row
        +cell #[code batch_size]
        +cell int
        +cell The number of texts to buffer.

    +footrow
        +cell yield
        +cell #[code Doc]
        +cell Documents in the order of the original text.

+h(2, "to_disk") Language.to_disk
    +tag method

p Save the current state to a directory.

+aside-code("Example").
    nlp.to_disk('/path/to/models')

+table(["Name", "Type", "Description"])
    +row
        +cell #[code path]
        +cell unicode or #[code Path]
        +cell
            |  A path to a directory, which will be created if it doesn't exist.
            |  Paths may be either strings or #[code Path]-like objects.

    +row
        +cell #[code **exclude]
        +cell -
        +cell Named attributes to prevent from being saved.

+h(2, "from_disk") Language.from_disk
    +tag method

p Loads state from a directory. Modifies the object in place and returns it.

+aside-code("Example").
    from spacy.language import Language
    nlp = Language().from_disk('/path/to/models')

+table(["Name", "Type", "Description"])
    +row
        +cell #[code path]
        +cell unicode or #[code Path]
        +cell
            |  A path to a directory. Paths may be either strings or
            |  #[code Path]-like objects.

    +row
        +cell #[code **exclude]
        +cell -
        +cell Named attributes to prevent from being loaded.

    +footrow
        +cell return
        +cell #[code Language]
        +cell The modified #[code Language] object.

+h(2, "to_bytes") Language.to_bytes
    +tag method

p Serialize the current state to a binary string.

+aside-code("Example").
    nlp_bytes = nlp.to_bytes()

+table(["Name", "Type", "Description"])
    +row
        +cell #[code **exclude]
        +cell -
        +cell Named attributes to prevent from being serialized.

    +footrow
        +cell return
        +cell bytes
        +cell The serialized form of the #[code Language] object.

+h(2, "from_bytes") Language.from_bytes
    +tag method

p Load state from a binary string.

+aside-code("Example").
    fron spacy.lang.en import English
    nlp_bytes = nlp.to_bytes()
    nlp2 = English()
    nlp2.from_bytes(nlp_bytes)

+table(["Name", "Type", "Description"])
    +row
        +cell #[code bytes_data]
        +cell bytes
        +cell The data to load from.

    +row
        +cell #[code **exclude]
        +cell -
        +cell Named attributes to prevent from being loaded.

    +footrow
        +cell return
        +cell bytes
        +cell The serialized form of the #[code Language] object.

+h(2, "attributes") Attributes

+table(["Name", "Type", "Description"])
    +row
        +cell #[code vocab]
        +cell #[code Vocab]
        +cell A container for the lexical types.

    +row
        +cell #[code tokenizer]
        +cell #[code Tokenizer]
        +cell Find word boundaries and create #[code Doc] object.

    +row
        +cell #[code tagger]
        +cell #[code Tagger]
        +cell Annotate #[code Doc] objects with POS tags.

    +row
        +cell #[code parser]
        +cell #[code DependencyParser]
        +cell Annotate #[code Doc] objects with syntactic dependencies.

    +row
        +cell #[code entity]
        +cell #[code EntityRecognizer]
        +cell Annotate #[code Doc] objects with named entities.

    +row
        +cell #[code matcher]
        +cell #[code Matcher]
        +cell Rule-based sequence matcher.

    +row
        +cell #[code make_doc]
        +cell #[code lambda text: Doc]
        +cell Create a #[code Doc] object from unicode text.

    +row
        +cell #[code pipeline]
        +cell -
        +cell Sequence of annotation functions.