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176 lines
6.9 KiB
ReStructuredText
176 lines
6.9 KiB
ReStructuredText
Quick Start
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===========
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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 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 :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', 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 models are
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lazy-loaded. This takes a few seconds on my machine.).
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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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Iteration
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:py:meth:`Tokens.__iter__` and :py:meth:`Tokens.__getitem__`
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- Most "Pythonic"
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- `spacy.tokens.Token` object, attribute access
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- Inefficient: New Token object created each time.
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Export
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:py:meth:`Tokens.count_by` and :py:meth:`Tokens.to_array`
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- `count_by`: Efficient dictionary of counts, for bag-of-words model.
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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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- 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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-----------------------------
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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: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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.. 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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Features
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--------
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+--------------------------------------------------------------------------+
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| Boolean Features |
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+----------+---------------------------------------------------------------+
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| IS_ALPHA | :py:meth:`str.isalpha` |
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+----------+---------------------------------------------------------------+
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| IS_DIGIT | :py:meth:`str.isdigit` |
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+----------+---------------------------------------------------------------+
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| IS_LOWER | :py:meth:`str.islower` |
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+----------+---------------------------------------------------------------+
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| IS_SPACE | :py:meth:`str.isspace` |
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+----------+---------------------------------------------------------------+
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| IS_TITLE | :py:meth:`str.istitle` |
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+----------+---------------------------------------------------------------+
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| IS_UPPER | :py:meth:`str.isupper` |
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+----------+---------------------------------------------------------------+
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| IS_ASCII | all(ord(c) < 128 for c in string) |
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+----------+---------------------------------------------------------------+
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| IS_PUNCT | all(unicodedata.category(c).startswith('P') for c in string) |
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+----------+---------------------------------------------------------------+
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| LIKE_URL | Using various heuristics, does the string resemble a URL? |
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+----------+---------------------------------------------------------------+
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| LIKE_NUM | "Two", "10", "1,000", "10.54", "1/2" etc all match |
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+----------+---------------------------------------------------------------+
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| ID of string features |
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+----------+---------------------------------------------------------------+
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| SIC | The original string, unmodified. |
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+----------+---------------------------------------------------------------+
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| NORM1 | The string after level 1 normalization: case, spelling |
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+----------+---------------------------------------------------------------+
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| NORM2 | The string after level 2 normalization |
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+----------+---------------------------------------------------------------+
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| SHAPE | Word shape, e.g. 10 --> dd, Garden --> Xxxx, Hi!5 --> Xx!d |
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+----------+---------------------------------------------------------------+
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| PREFIX | A short slice from the start of the string. |
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+----------+---------------------------------------------------------------+
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| SUFFIX | A short slice from the end of the string. |
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+----------+---------------------------------------------------------------+
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| CLUSTER | Brown cluster ID of the word |
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+----------+---------------------------------------------------------------+
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| LEMMA | The word's lemma, i.e. morphological suffixes removed |
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+----------+---------------------------------------------------------------+
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| TAG | The word's part-of-speech tag |
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+----------+---------------------------------------------------------------+
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