//- 💫 DOCS > API > LANGUAGE

include ../../_includes/_mixins

p A text processing pipeline.

+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.


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

p Create or load the pipeline.

+table(["Name", "Type", "Description"])
    +row
        +cell #[code **overrides]
        +cell -
        +cell Keyword arguments indicating which defaults to override.

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

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

p Apply the pipeline to a single text.

+aside-code("Example").
    from spacy.en import English
    nlp = English()
    doc = nlp('An example sentence. Another example sentence.')
    doc[0].orth_, doc[0].head.tag_
    # ('An', 'NN')

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

    +row
        +cell #[code tag]
        +cell bool
        +cell Whether to apply the part-of-speech tagger.

    +row
        +cell #[code parse]
        +cell bool
        +cell Whether to apply the syntactic dependency parser.

    +row
        +cell #[code entity]
        +cell bool
        +cell Whether to apply the named entity recognizer.

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

+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 Containers for accessing the linguistic annotations.

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

p Save the #[code Vocab], #[code StringStore] and pipeline to a directory.

+table(["Name", "Type", "Description"])
    +row
        +cell #[code path]
        +cell string or pathlib path
        +cell Path to save the model.

    +footrow
        +cell return
        +cell #[code None]
        +cell -