mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
49 lines
2.2 KiB
Plaintext
49 lines
2.2 KiB
Plaintext
//- 💫 DOCS > USAGE > SPACY 101 > SIMILARITY
|
||
|
||
p
|
||
| spaCy is able to compare two objects, and make a prediction of
|
||
| #[strong how similar they are]. Predicting similarity is useful for
|
||
| building recommendation systems or flagging duplicates. For example, you
|
||
| can suggest a user content that's similar to what they're currently
|
||
| looking at, or label a support ticket as a duplicate if it's very
|
||
| similar to an already existing one.
|
||
|
||
p
|
||
| Each #[code Doc], #[code Span] and #[code Token] comes with a
|
||
| #[+api("token#similarity") #[code .similarity()]] method that lets you
|
||
| compare it with another object, and determine the similarity. Of course
|
||
| similarity is always subjective – whether "dog" and "cat" are similar
|
||
| really depends on how you're looking at it. spaCy's similarity model
|
||
| usually assumes a pretty general-purpose definition of similarity.
|
||
|
||
+code.
|
||
tokens = nlp(u'dog cat banana')
|
||
|
||
for token1 in tokens:
|
||
for token2 in tokens:
|
||
print(token1.similarity(token2))
|
||
|
||
+aside
|
||
| #[strong #[+procon("neutral", "identical", false, 16)] similarity:] identical#[br]
|
||
| #[strong #[+procon("yes", "similar", false, 16)] similarity:] similar (higher is more similar) #[br]
|
||
| #[strong #[+procon("no", "dissimilar", false, 16)] similarity:] dissimilar (lower is less similar)
|
||
|
||
+table
|
||
+row("head")
|
||
for column in ["", "dog", "cat", "banana"]
|
||
+head-cell.u-text-center=column
|
||
each cells, label in {"dog": [1, 0.8, 0.24], "cat": [0.8, 1, 0.28], "banana": [0.24, 0.28, 1]}
|
||
+row
|
||
+cell.u-text-label.u-color-theme=label
|
||
for cell in cells
|
||
+cell.u-text-center
|
||
- var result = cell > 0.5 ? ["yes", "similar"] : cell != 1 ? ["no", "dissimilar"] : ["neutral", "identical"]
|
||
| #[code=cell.toFixed(2)] #[+procon(...result)]
|
||
|
||
p
|
||
| In this case, the model's predictions are pretty on point. A dog is very
|
||
| similar to a cat, whereas a banana is not very similar to either of them.
|
||
| Identical tokens are obviously 100% similar to each other (just not always
|
||
| exactly #[code 1.0], because of vector math and floating point
|
||
| imprecisions).
|