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43 lines
1.6 KiB
Plaintext
43 lines
1.6 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. The default
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| #[+a("/docs/usage/models#available") English model] installs
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| 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("Tip: Training a word2vec model")
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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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+h(2, "101") Similarity and word vectors 101
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+tag-model("vectors")
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include _spacy-101/_similarity
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include _spacy-101/_word-vectors
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+h(2, "custom") Customising word vectors
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+under-construction
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p
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| By default, #[+api("token#vector") #[code Token.vector]] returns the
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| vector for its underlying #[+api("lexeme") #[code Lexeme]], while
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| #[+api("doc#vector") #[code Doc.vector]] and
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| #[+api("span#vector") #[code Span.vector]] return an average of the
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| 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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+h(2, "similarity") Similarity
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+under-construction
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