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* Work on quickstart
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@ -5,53 +5,70 @@ Quick Start
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Install
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-------
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.. code:: bash
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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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The download command fetches and installs the parser model and word representations,
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which are too big to host on PyPi (about 100mb each). The data is installed within
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the spacy.en package directory.
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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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The main entry-point is :py:meth:`spacy.en.English.__call__`, which accepts a unicode string as an argument, and returns a :py:class:`spacy.tokens.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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>>> tokens = nlp(u'A fine, very fine, example sentence', tag=True,
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parse=True)
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Calls to :py:meth:`English.__call__` has a side-effect: when a new
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word is seen, it is added to the string-to-ID mapping table in
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:py:class:`English.vocab.strings`. Because of this, you will usually only want
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to create one instance of the pipeline. If you create two instances, and use
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them to process different text, you'll probably get different string-to-ID
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mappings. You might choose to wrap the English class as a singleton to ensure
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only one instance is created, but I've left that up to you. I prefer to pass
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the instance around as an explicit argument.
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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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overhead (apart from the first call, when the tagger and parser models are
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lazy-loaded. This takes a few seconds on my machine.).
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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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:py:meth:`English.__class__` returns a :py:class:`Tokens` object, through which
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you'll access the processed text. You can access the text in three ways:
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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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Iteration
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:py:meth:`Tokens.__iter__` and :py:meth:`Tokens.__getitem__`
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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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- Most "Pythonic"
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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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- `spacy.tokens.Token` object, attribute access
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>>> from spacy.en import enums
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>>> tokens.to_array([enums.LEMMA, enums.SIC])
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- Inefficient: New Token object created each time.
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Another common operation is to export the embeddings vector to a numpy array:
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Export
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:py:meth:`Tokens.count_by` and :py:meth:`Tokens.to_array`
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>>> tokens.to_vec()
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- `count_by`: Efficient dictionary of counts, for bag-of-words model.
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Create a bag-of-words representation:
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- `to_array`: Export to numpy array. One row per word, one column per
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attribute.
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>>> tokens.count_by(enums.LEMMA)
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- Specify attributes with constants from `spacy.en.attrs`.
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Cython
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:py:attr:`TokenC* Tokens.data`
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- Raw data is stored in contiguous array of structs
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- Good syntax, C speed
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- Documentation coming soon. In the meantime, see spacy/syntax/_parser.features.pyx
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or spacy/en/pos.pyx
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(Most of the) API at a glance
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@ -61,6 +78,10 @@ Create a bag-of-words representation:
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.. py:method:: __call__(self, text: unicode, tag=True, parse=False) --> Tokens
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.. py:method:: vocab.__getitem__(self, text: unicode) --> Lexeme
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.. py:method:: vocab.__getitem__(self, text: unicode) --> Lexeme
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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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