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