spaCy/website/docs/_api-english.jade

//-  Docs > API > English
//- ============================================================================

+section('english')
    +h2('english', 'https://github.com/' + profiles.github + '/spaCy/blob/master/spacy/language.py#L40')
        | #[+label('tag') class] English(Language)

    p.
        The English analysis pipeline. Usually you'll load this once per process,
        and pass the instance around your program.
    
    +code('python', 'overview').
        class Language:
            lang = None
            def __init__(self, data_dir=None, tokenizer=None, tagger=None, parser=None, entity=None, matcher=None):
                return self
            
            def __call__(self, text, tag=True, parse=True, entity=True):
                return Doc()

            def pipe(self, texts_iterator, batch_size=1000, n_threads=2):
                yield Doc()

            def end_training(self, data_dir=None):
                return None

        class English(Language):
            lang = 'en'

        class German(Language):
            lang = 'de'

    +section('english-init')
        +h3('english-init')
            | #[+label('tag') method] English.__init__
            
        p
            | Load the pipeline.  Each component can be passed
            | as an argument, or left as #[code None], in which case it will be loaded
            | from a classmethod, named e.g. #[code default_vocab()].
            
            +aside("Efficiency").
                Loading takes 10-20 seconds, and the instance consumes 2 to 3 
                gigabytes of memory.  Intended use is for one instance to be
                created for each language per process, but you can create more
                if you're doing something unusual. You may wish to make the
                instance a global variable or 'singleton'.

        +table(['Example', 'Description'], 'code')
            +row
                +cell #[code.lang-python nlp = English()]
                +cell Load everything, from default package

            +row
                +cell #[code.lang-python nlp = English(data_dir='my_data')]
                +cell Load everything, from specified dir

            +row
                +cell #[code.lang-python nlp = English(parser=False)]
                +cell Load everything except the parser.

            +row
                +cell #[code.lang-python nlp = English(parser=False, tagger=False)]
                +cell Load everything except the parser and tagger.

            +row
                +cell #[code.lang-python nlp = English(parser=MyParser())]
                +cell Supply your own parser

        +code('python', 'Definition').
            def __init__(self, data_dir=None, tokenizer=None, tagger=None, parser=None, entity=None, matcher=None):
                return self
           
        +table(['Arg', 'Type', 'Description'], 'params')
            +row
                +cell data_dir
                +cell str
                +cell.
                    The data directory. If None, value is obtained via the 
                    #[code default_data_dir()] method.

            +row
                +cell vocab
                +cell #[code Vocab]
                +cell.
                    The vocab object, which should be an instance of class 
                    #[code spacy.vocab.Vocab]. If #[code None], the object is 
                    obtained from the #[code default_vocab()] class method. The 
                    vocab object manages all of the language specific rules and 
                    definitions, maintains the cache of lexical types, and manages 
                    the word vectors. Because the vocab owns this important data, 
                    most objects hold a reference to the vocab.

            +row
                +cell tokenizer
                +cell #[code Tokenizer]
                +cell.
                    The tokenizer, which should be a callable that accepts a 
                    unicode string, and returns a #[code Doc] object. If set to 
                    #[code None], the default tokenizer is constructed from the 
                    #[code default_tokenizer()] method.

            +row
                +cell tagger
                +cell #[code Tagger]
                +cell.
                    The part-of-speech tagger, which should be a callable that 
                    accepts a #[code Doc] object, and sets the part-of-speech 
                    tags in-place. If set to None, the default tagger is constructed 
                    from the #[code default_tagger()] method.

            +row
                +cell parser
                +cell #[code Parser]
                +cell.
                    The dependency parser, which should be a callable that accepts 
                    a #[code Doc] object, and sets the sentence boundaries,
                    syntactic heads and dependency labels in-place.
                    If set to #[code None], the default parser is 
                    constructed from the #[code default_parser()] method. To disable
                    the parser and prevent it from being loaded, pass #[code parser=False].

            +row
                +cell entity
                +cell #[code Parser]
                +cell.
                    The named entity recognizer, which should be a callable that 
                    accepts a #[code Doc] object, and sets the named entity annotations 
                    in-place. If set to None, the default entity recognizer is 
                    constructed from the #[code default_entity()] method. To disable
                    the entity recognizer and prevent it from being loaded, pass
                    #[code entity=False].

            +row
                +cell matcher
                +cell #[code Matcher]
                +cell.
                    The pattern matcher, which should be a callable that accepts 
                    a #[code Doc] object, and sets named entity annotations in-place
                    using token-based rules. If set 
                    to None, the default matcher is constructed from the 
                    #[code default_matcher()] method.

        +section('english-call')
            +h3('english-call')
                | #[+label('tag') method] English.__call__

            p
                | The main entry point to spaCy. Takes raw unicode text, and returns 
                | a #[code Doc] object, which can be iterated to access #[code Token] 
                | and #[code Span] objects.
                
                +aside("Efficiency").
                    spaCy's algorithms are all linear-time, so you can supply 
                    documents of arbitrary length, e.g. whole novels.

            +table(['Example', 'Description'], 'code')
                +row
                    +cell #[code.lang-python doc = nlp(u'Some text.')]
                    +cell Apply the full pipeline.
                +row
                    +cell #[code.lang-python doc = nlp(u'Some text.', parse=False)]
                    +cell Applies tagger and entity, not parser
                +row
                    +cell #[code.lang-python doc = nlp(u'Some text.', entity=False)]
                    +cell Applies tagger and parser, not entity.
                +row
                    +cell #[code.lang-python doc = nlp(u'Some text.', tag=False)]
                    +cell Does not apply tagger, entity or parser
                +row
                    +cell #[code.lang-python doc = nlp(u'')]
                    +cell Zero-length tokens, not an error
                +row
                    +cell #[code.lang-python doc = nlp(b'Some text')]
                    +cell Error: need unicode
                +row
                    +cell #[code.lang-python doc = nlp(b'Some text'.decode('utf8'))]
                    +cell Decode bytes into unicode first.

            +code('python', 'Definition').
                def __call__(self, text, tag=True, parse=True, entity=True, matcher=True):
                    return self

            +table(['Name', 'Type', 'Description'], 'params')
                +row
                    +cell text
                    +cell #[a(href=link_unicode target='_blank') unicode]
                    +cell.
                        The text to be processed. spaCy expects raw unicode text 
                        – you don't necessarily need to, say, split it into paragraphs. 
                        However, depending on your documents, you might be better 
                        off applying custom pre-processing. Non-text formatting, 
                        e.g. from HTML mark-up, should be removed before sending 
                        the document to spaCy. If your documents have a consistent 
                        format, you may be able to improve accuracy by pre-processing. 
                        For instance, if the first word of your documents are always 
                        in upper-case, it may be helpful to normalize them before 
                        supplying them to spaCy.

                +row
                    +cell tag
                    +cell #[a(href=link_bool target='_blank') bool]
                    +cell.
                        Whether to apply the part-of-speech tagger. Required for 
                        parsing and entity recognition.

                +row
                    +cell parse
                    +cell #[a(href=link_bool target='_blank') bool]
                    +cell.
                        Whether to apply the syntactic dependency parser.

                +row
                    +cell entity
                    +cell #[a(href=link_bool target='_blank') bool]
                    +cell.
                        Whether to apply the named entity recognizer.

        +section('english-pipe')
            +h3('english-pipe')
                | #[+label('tag') method] English.pipe

            p
                | Parse a sequence of texts into a sequence of #[code Doc] objects. 
                | Accepts a generator as input, and produces a generator as output. 
                | Internally, it accumulates a buffer of #[code batch_size] 
                | texts, works on them with #[code n_threads] workers in parallel, 
                | and then yields the #[code Doc] objects one by one.
                
                +aside('Efficiency').
                    spaCy releases the global interpreter lock around the parser and 
                    named entity recognizer, allowing shared-memory parallelism via 
                    OpenMP. However, OpenMP is not supported on OSX — so multiple 
                    threads will only be used on Linux and Windows.

            +table(["Example", "Description"], 'usage')
                +row
                    +cell #[a(href='https://github.com/' + profiles.github + '/spaCy/blob/master/examples/parallel_parse.py' target='_blank') parallel_parse.py]
                    +cell Parse comments from Reddit in parallel.

            +code('python', 'Definition').
                def pipe(self, texts, n_threads=2, batch_size=1000):
                    yield Doc()

            +table(['Arg', 'Type', 'Description'], 'params')
                +row
                    +cell texts
                    +cell
                    +cell.
                        A sequence of unicode objects. Usually you will want this 
                        to be a generator, so that you don't need to have all of 
                        your texts in memory.

                +row
                    +cell n_threads
                    +cell #[a(href=link_int target='_blank') int]
                    +cell.
                        The number of worker threads to use. If -1, OpenMP will 
                        decide how many to use at run time. Default is 2.

                +row
                    +cell batch_size
                    +cell #[a(href=link_int target='_blank') int]
                    +cell.
                        The number of texts to buffer. Let's say you have a 
                        #[code batch_size] of 1,000. The input, #[code texts], is 
                        a generator that yields the texts one-by-one. We want to 
                        operate on them in parallel. So, we accumulate a work queue. 
                        Instead of taking one document from #[code texts] and 
                        operating on it, we buffer #[code batch_size] documents, 
                        work on them in parallel, and then yield them one-by-one. 
                        Higher #[code batch_size] therefore often results in better
                        parallelism, up to a point.