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110 lines
2.7 KiB
ReStructuredText
110 lines
2.7 KiB
ReStructuredText
Quick Start
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===========
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Install
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-------
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$ pip install spacy
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$ python -m spacy.en.download
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The download command fetches the parser model, which is too big to host on PyPi
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(about 100mb). The data is installed within the spacy.en package.
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Usage
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-----
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The main entry-point is spacy.en.English.__call__, which you use to turn
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a unicode string into a Tokens object:
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>>> from spacy.en import English
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>>> nlp = English()
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>>> tokens = nlp(u'A fine, very fine, example sentence')
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You shouldn't need to batch up your text or prepare it in any way.
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Processing times are linear in the length of the string, with minimal per-call
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overhead (apart from the first call, when the tagger and parser are lazy-loaded).
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Usually, you will only want to create one instance of the pipeline, and pass it
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around. Each instance maintains its own string-to-id mapping table, so if you
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process a new word, it is likely to be assigned different integer IDs by the
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two different instances.
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The Tokens object has a sequences interface, which you can use to get
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individual tokens:
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>>> print tokens[0].lemma
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'a'
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>>> for token in tokens:
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... print token.sic, token.pos
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For feature extraction, you can select a number of features to export to
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a numpy.ndarray:
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>>> from spacy.en import enums
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>>> tokens.to_array([enums.LEMMA, enums.SIC])
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Another common operation is to export the embeddings vector to a numpy array:
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>>> tokens.to_vec()
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Create a bag-of-words representation:
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>>> tokens.count_by(enums.LEMMA)
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(Most of the) API at a glance
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-----------------------------
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.. py:class:: spacy.en.English(self, data_dir=join(dirname(__file__), 'data'))
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.. py:method:: __call__(self, text: unicode, tag=True, parse=False) --> Tokens
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.. py:class:: spacy.tokens.Tokens via English.__call__
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.. py:method:: __getitem__(self, i) --> Token
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.. py:method:: __iter__(self) --> Iterator[Token]
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.. py:method:: to_array(self, attr_ids: List[int]) --> numpy.ndarray[ndim=2, dtype=int32]
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.. py:method:: count_by(self, attr_id: int) --> Dict[int, int]
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.. py:class:: spacy.tokens.Token via Tokens.__iter__, Tokens.__getitem__
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.. py:method:: __unicode__(self) --> unicode
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.. py:method:: __len__(self) --> int
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.. py:method:: nbor(self, i=1) --> Token
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.. py:method:: child(self, i=1) --> Token
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.. py:method:: sibling(self, i=1) --> Token
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.. py:method:: check_flag(self, attr_id: int) --> bool
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.. py:attribute:: cluster: int
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.. py:attribute:: string: unicode
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.. py:attribute:: string: unicode
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.. py:attribute:: lemma: unicode
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.. py:attribute:: dep_tag: unicode
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.. py:attribute:: pos: unicode
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.. py:attribute:: fine_pos: unicode
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.. py:attribute:: sic: unicode
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.. py:attribute:: head: Token
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