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* Play with examples in index.rst

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Matthew Honnibal 2014-12-23 15:17:56 +11:00
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docs/source/index.rst
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@ -3,9 +3,9 @@
You can adapt this file completely to your liking, but it should at least You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive. contain the root `toctree` directive.
================================ ===================================
spaCy: Industrial-strength NLP spaCy: Text-processing for products
================================ ===================================
spaCy is a library for industrial-strength text processing in Python and Cython. spaCy is a library for industrial-strength text processing in Python and Cython.
Its core values are efficiency, accuracy and minimalism: you get a fast pipeline of Its core values are efficiency, accuracy and minimalism: you get a fast pipeline of
@ -15,22 +15,23 @@ spaCy is particularly good for feature extraction, because it pre-loads lexical
resources, maps strings to integer IDs, and supports output of numpy arrays: resources, maps strings to integer IDs, and supports output of numpy arrays:
>>> from spacy.en import English >>> from spacy.en import English
>>> from spacy.en import attrs
>>> nlp = English() >>> nlp = English()
>>> tokens = nlp(u'An example sentence', pos_tag=True, parse=True) >>> tokens = nlp(u'An example sentence', tag=True, parse=True)
>>> tokens.to_array((attrs.LEMMA, attrs.POS, attrs.SHAPE, attrs.CLUSTER)) >>> from spacy.en import attrs
>>> feats = tokens.to_array((attrs.LEMMA, attrs.POS, attrs.SHAPE, attrs.CLUSTER))
>>> for lemma, pos, shape, cluster in feats:
... print nlp.strings[lemma], nlp.tagger.tags[pos], nlp.strings[shape], cluster
spaCy also makes it easy to add in-line mark up. Let's say you want to mark all spaCy also makes it easy to add in-line mark up. Let's say you want to mark all
adverbs in red: adverbs in red:
>>> from spacy.defs import ADVERB >>> from spacy.defs import ADVERB
>>> color = lambda t: u'\033[91m' % t if t.pos == ADVERB else u'%s' >>> color = lambda t: u'\033[91m' % t if t.pos == ADVERB else u'%s'
>>> print u''.join(color(t) + unicode(t) for t in tokens) >>> print u''.join(color(token) + unicode(token) for t in tokens)
Tokens.__iter__ produces a sequence of Token objects. The Token.__unicode__ Easy. The trick here is that the Token objects know to pad themselves with
method --- invoked by unicode(t) --- pads each token with any whitespace that whitespace when you ask for their unicode representation, so you can always get
followed it. So, u''.join(unicode(t) for t in tokens) is guaranteed to restore back the original string.
the original string.
spaCy is also very efficient --- much more efficient than any other language spaCy is also very efficient --- much more efficient than any other language
processing tools available. The table below compares the time to tokenize, POS processing tools available. The table below compares the time to tokenize, POS
@ -61,6 +62,12 @@ and what you're competing to do is write papers --- so it's very hard to write
software useful to non-academics. Seeing this gap, I resigned from my post-doc, software useful to non-academics. Seeing this gap, I resigned from my post-doc,
and wrote spaCy. and wrote spaCy.
spaCy is dual-licensed: you can either use it under the GPL, or pay a one-time
fee of $5000 for a commercial license. I think this is excellent value:
you'll find NLTK etc much more expensive, because what you save on license
cost, you'll lose many times over in lost productivity. $5000 does not buy you
much developer time.
.. toctree:: .. toctree::
:hidden: :hidden:
:maxdepth: 3 :maxdepth: 3