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Matthew Honnibal 2015-01-25 23:05:35 +11:00
parent 30a02f2611
commit 6f1b364b32
1 changed files with 5 additions and 5 deletions
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docs/source/quickstart.rst
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@ -39,18 +39,18 @@ as an argument, and returns a :py:class:`tokens.Tokens` object. You can
iterate over it to get :py:class:`tokens.Token` objects, which provide
a convenient API:
>>> from __future__ import unicode_literals # If Python 2
>>> from spacy.en import English
>>> nlp = English()
>>> tokens = nlp(u'I ate the pizza with anchovies.')
>>> pizza = tokens[3]
>>> (pizza.orth, pizza.orth_, pizza.head.lemma, pizza.head.lemma_)
... (14702, u'pizza', 14702, u'ate')
... (14702, 'pizza', 14702, 'ate')
spaCy maps all strings to sequential integer IDs --- a common idiom in NLP.
spaCy maps all strings to sequential integer IDs --- a common trick in NLP.
If an attribute `Token.foo` is an integer ID, then `Token.foo_` is the string,
e.g. `pizza.orth_` and `pizza.orth` provide the integer ID and the string of
the original orthographic form of the word, with no string normalizations
applied.
the original orthographic form of the word.
.. note:: en.English.__call__ is stateful --- it has an important **side-effect**.
@ -141,7 +141,7 @@ Features
>>> lexeme = nlp.vocab[u'Apple']
>>> lexeme.is_alpha, is_upper
True, False
>>> tokens = nlp(u'Apple computers')
>>> tokens = nlp('Apple computers')
>>> tokens[0].is_alpha, tokens[0].is_upper
>>> True, False
>>> from spact.en.attrs import IS_ALPHA, IS_UPPER