spaCy/website/docs/usage/_spacy-101/_word-vectors.jade

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2017-05-24 00:16:31 +03:00
//- 💫 DOCS > USAGE > SPACY 101 > WORD VECTORS
p
| Similarity is determined by comparing #[strong word vectors] or "word
| embeddings", multi-dimensional meaning representations of a word. Word
| vectors can be generated using an algorithm like
| #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]. Most of spaCy's
| #[+a("/docs/usage/models") default models] come with
| #[strong 300-dimensional vectors] that look like this:
2017-05-24 00:16:31 +03:00
+code("banana.vector", false, false, 250).
array([2.02280000e-01, -7.66180009e-02, 3.70319992e-01,
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3.66849989e-01, 2.52470002e-03, -6.40089989e-01,
-2.97650009e-01, 7.89430022e-01, 3.31680000e-01,
-1.19659996e+00, -4.71559986e-02, 5.31750023e-01], dtype=float32)
p
| The #[code .vector] attribute will return an object's vector.
| #[+api("doc#vector") #[code Doc.vector]] and
| #[+api("span#vector") #[code Span.vector]] will default to an average
| of their token vectors. You can also check if a token has a vector
| assigned, and get the L2 norm, which can be used to normalise
| vectors.
+code.
tokens = nlp(u'dog cat banana sasquatch')
for token in tokens:
print(token.text, token.has_vector, token.vector_norm, token.is_oov)
+aside
| #[strong Text]: The original token text.#[br]
| #[strong has vector]: Does the token have a vector representation?#[br]
| #[strong Vector norm]: The L2 norm of the token's vector (the square root
| of the sum of the values squared)#[br]
| #[strong is OOV]: Is the word out-of-vocabulary?
+table(["Text", "Has vector", "Vector norm", "OOV"])
- var style = [0, 1, 1, 1]
+annotation-row(["dog", true, 7.033672992262838, false], style)
+annotation-row(["cat", true, 6.68081871208896, false], style)
+annotation-row(["banana", true, 6.700014292148571, false], style)
+annotation-row(["sasquatch", false, 0, true], style)
p
| The words "dog", "cat" and "banana" are all pretty common in English, so
| they're part of the model's vocabulary, and come with a vector. The word
| "sasquatch" on the other hand is a lot less common and out-of-vocabulary
| so its vector representation consists of 300 dimensions of #[code 0],
| which means it's practically nonexistent.
p
| If your application will benefit from a large vocabulary with more
| vectors, you should consider using one of the
| #[+a("/docs/usage/models#available") larger models] instead of the default,
| smaller ones, which usually come with a clipped vocabulary.