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16 lines
758 B
Plaintext
16 lines
758 B
Plaintext
//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > BASICS
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+aside("Training word vectors")
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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("/models/en") English model] installs
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| 300-dimensional vectors trained on the
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| #[+a("http://commoncrawl.org") Common Crawl] corpus.
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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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include ../_spacy-101/_similarity
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include ../_spacy-101/_word-vectors
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