mirror of https://github.com/explosion/spaCy.git
103 lines
4.3 KiB
Markdown
103 lines
4.3 KiB
Markdown
import Infobox from 'components/infobox'
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Similarity is determined by comparing **word vectors** or "word embeddings",
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multi-dimensional meaning representations of a word. Word vectors can be
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generated using an algorithm like
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[word2vec](https://en.wikipedia.org/wiki/Word2vec) and usually look like this:
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```python
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### banana.vector
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array([2.02280000e-01, -7.66180009e-02, 3.70319992e-01,
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3.28450017e-02, -4.19569999e-01, 7.20689967e-02,
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-3.74760002e-01, 5.74599989e-02, -1.24009997e-02,
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5.29489994e-01, -5.23800015e-01, -1.97710007e-01,
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-3.41470003e-01, 5.33169985e-01, -2.53309999e-02,
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1.73800007e-01, 1.67720005e-01, 8.39839995e-01,
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5.51070012e-02, 1.05470002e-01, 3.78719985e-01,
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2.42750004e-01, 1.47449998e-02, 5.59509993e-01,
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1.25210002e-01, -6.75960004e-01, 3.58420014e-01,
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# ... and so on ...
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3.66849989e-01, 2.52470002e-03, -6.40089989e-01,
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-2.97650009e-01, 7.89430022e-01, 3.31680000e-01,
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-1.19659996e+00, -4.71559986e-02, 5.31750023e-01], dtype=float32)
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```
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<Infobox title="Important note" variant="warning">
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To make them compact and fast, spaCy's small [models](/models) (all packages
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that end in `sm`) **don't ship with word vectors**, and only include
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context-sensitive **tensors**. This means you can still use the `similarity()`
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methods to compare documents, spans and tokens – but the result won't be as
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good, and individual tokens won't have any vectors assigned. So in order to use
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_real_ word vectors, you need to download a larger model:
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```diff
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- python -m spacy download en_core_web_sm
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+ python -m spacy download en_core_web_lg
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```
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</Infobox>
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Models that come with built-in word vectors make them available as the
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[`Token.vector`](/api/token#vector) attribute. [`Doc.vector`](/api/doc#vector)
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and [`Span.vector`](/api/span#vector) will default to an average of their token
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vectors. You can also check if a token has a vector assigned, and get the L2
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norm, which can be used to normalize vectors.
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```python
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### {executable="true"}
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import spacy
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nlp = spacy.load('en_core_web_md')
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tokens = nlp(u'dog cat banana afskfsd')
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for token in tokens:
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print(token.text, token.has_vector, token.vector_norm, token.is_oov)
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```
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> - **Text**: The original token text.
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> - **has vector**: Does the token have a vector representation?
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> - **Vector norm**: The L2 norm of the token's vector (the square root of the
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> sum of the values squared)
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> - **OOV**: Out-of-vocabulary
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The words "dog", "cat" and "banana" are all pretty common in English, so they're
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part of the model's vocabulary, and come with a vector. The word "afskfsd" on
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the other hand is a lot less common and out-of-vocabulary – so its vector
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representation consists of 300 dimensions of `0`, which means it's practically
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nonexistent. If your application will benefit from a **large vocabulary** with
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more vectors, you should consider using one of the larger models or loading in a
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full vector package, for example,
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[`en_vectors_web_lg`](/models/en#en_vectors_web_lg), which includes over **1
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million unique vectors**.
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spaCy is able to compare two objects, and make a prediction of **how similar
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they are**. Predicting similarity is useful for building recommendation systems
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or flagging duplicates. For example, you can suggest a user content that's
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similar to what they're currently looking at, or label a support ticket as a
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duplicate if it's very similar to an already existing one.
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Each `Doc`, `Span` and `Token` comes with a
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[`.similarity()`](/api/token#similarity) method that lets you compare it with
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another object, and determine the similarity. Of course similarity is always
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subjective – whether "dog" and "cat" are similar really depends on how you're
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looking at it. spaCy's similarity model usually assumes a pretty general-purpose
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definition of similarity.
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```python
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### {executable="true"}
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import spacy
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nlp = spacy.load('en_core_web_md') # make sure to use larger model!
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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.text, token2.text, token1.similarity(token2))
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```
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In this case, the model's predictions are pretty on point. A dog is very similar
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to a cat, whereas a banana is not very similar to either of them. Identical
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tokens are obviously 100% similar to each other (just not always exactly `1.0`,
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because of vector math and floating point imprecisions).
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