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45 lines
2.0 KiB
Plaintext
45 lines
2.0 KiB
Plaintext
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//- 💫 DOCS > USAGE > SPACY 101 > SIMILARITY
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p
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| spaCy is able to compare two objects, and make a prediction of
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| #[strong how similar they are]. Predicting similarity is useful for
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| building recommendation systems or flagging duplicates. For example, you
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| can suggest a user content that's similar to what they're currently
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| looking at, or label a support ticket as a duplicate, if it's very
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| similar to an already existing one.
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p
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| Each #[code Doc], #[code Span] and #[code Token] comes with a
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| #[+api("token#similarity") #[code .similarity()]] method that lets you
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| compare it with another object, and determine the similarity. Of course
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| similarity is always subjective – whether "dog" and "cat" are similar
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| really depends on how you're looking at it. spaCy's similarity model
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| usually assumes a pretty general-purpose definition of similarity.
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+code.
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tokens = nlp(u'dog cat banana')
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for token1 in tokens:
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for token2 in tokens:
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print(token1.similarity(token2))
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+aside
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| #[strong #[+procon("neutral", 16)] similarity:] identical#[br]
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| #[strong #[+procon("pro", 16)] similarity:] similar (higher is more similar) #[br]
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| #[strong #[+procon("con", 16)] similarity:] dissimilar (lower is less similar)
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+table(["", "dog", "cat", "banana"])
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each cells, label in {"dog": [1.00, 0.80, 0.24], "cat": [0.80, 1.00, 0.28], "banana": [0.24, 0.28, 1.00]}
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+row
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+cell.u-text-label.u-color-theme=label
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for cell in cells
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+cell #[code=cell.toFixed(2)]
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| #[+procon(cell < 0.5 ? "con" : cell != 1 ? "pro" : "neutral")]
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p
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| In this case, the model's predictions are pretty on point. A dog is very
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| similar to a cat, whereas a banana is not very similar to either of them.
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| Identical tokens are obviously 100% similar to each other (just not always
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| exactly #[code 1.0], because of vector math and floating point
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| imprecisions).
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