mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
67 lines
2.5 KiB
Plaintext
67 lines
2.5 KiB
Plaintext
|
//- 💫 DOCS > USAGE > WORD VECTORS & SIMILARITIES
|
||
|
|
|
||
|
|
include ../../_includes/_mixins
|
||
|
|
|
||
|
|
p
|
||
|
|
| Dense, real valued vectors representing distributional similarity
|
||
|
|
| information are now a cornerstone of practical NLP. The most common way
|
||
|
|
| to train these vectors is the #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]
|
||
|
|
| family of algorithms.
|
||
|
|
|
||
|
|
+aside("Tip")
|
||
|
|
| If you need to train a word2vec model, we recommend the implementation in
|
||
|
|
| the Python library #[+a("https://radimrehurek.com/gensim/") Gensim].
|
||
|
|
|
||
|
|
p
|
||
|
|
| spaCy makes using word vectors very easy. The
|
||
|
|
| #[+api("lexeme") #[code Lexeme]], #[+api("token") #[code Token]],
|
||
|
|
| #[+api("span") #[code Span]] and #[+api("doc") #[code Doc]] classes all
|
||
|
|
| have a #[code .vector] property, which is a 1-dimensional numpy array of
|
||
|
|
| 32-bit floats:
|
||
|
|
|
||
|
|
+code.
|
||
|
|
import numpy
|
||
|
|
|
||
|
|
apples, and_, oranges = nlp(u'apples and oranges')
|
||
|
|
print(apples.vector.shape)
|
||
|
|
# (1,)
|
||
|
|
apples.similarity(oranges)
|
||
|
|
|
||
|
|
p
|
||
|
|
| By default, #[code Token.vector] returns the vector for its underlying
|
||
|
|
| lexeme, while #[code Doc.vector] and #[code Span.vector] return an
|
||
|
|
| average of the vectors of their tokens. You can customize these
|
||
|
|
| behaviours by modifying the #[code doc.user_hooks],
|
||
|
|
| #[code doc.user_span_hooks] and #[code doc.user_token_hooks]
|
||
|
|
| dictionaries.
|
||
|
|
|
||
|
|
+aside-code("Example").
|
||
|
|
# TODO
|
||
|
|
|
||
|
|
p
|
||
|
|
| The default English model installs vectors for one million vocabulary
|
||
|
|
| entries, using the 300-dimensional vectors trained on the Common Crawl
|
||
|
|
| corpus using the #[+a("http://nlp.stanford.edu/projects/glove/") GloVe]
|
||
|
|
| algorithm. The GloVe common crawl vectors have become a de facto
|
||
|
|
| standard for practical NLP.
|
||
|
|
|
||
|
|
+aside-code("Example").
|
||
|
|
# TODO
|
||
|
|
|
||
|
|
p
|
||
|
|
| You can load new word vectors from a file-like buffer using the
|
||
|
|
| #[code vocab.load_vectors()] method. The file should be a
|
||
|
|
| whitespace-delimited text file, where the word is in the first column,
|
||
|
|
| and subsequent columns provide the vector data. For faster loading, you
|
||
|
|
| can use the #[code vocab.vectors_from_bin_loc()] method, which accepts a
|
||
|
|
| path to a binary file written by #[code vocab.dump_vectors()].
|
||
|
|
|
||
|
|
+aside-code("Example").
|
||
|
|
# TODO
|
||
|
|
|
||
|
|
p
|
||
|
|
| You can also load vectors from memory, by writing to the #[code lexeme.vector]
|
||
|
|
| property. If the vectors you are writing are of different dimensionality
|
||
|
|
| from the ones currently loaded, you should first call
|
||
|
|
| #[code vocab.resize_vectors(new_size)].
|