spaCy/website/docs/usage/101/_vectors-similarity.md

import Infobox from 'components/infobox'

Similarity is determined by comparing word vectors or "word embeddings", multi-dimensional meaning representations of a word. Word vectors can be generated using an algorithm like word2vec and usually look like this:

### banana.vector
array([2.02280000e-01,  -7.66180009e-02,   3.70319992e-01,
       3.28450017e-02,  -4.19569999e-01,   7.20689967e-02,
      -3.74760002e-01,   5.74599989e-02,  -1.24009997e-02,
       5.29489994e-01,  -5.23800015e-01,  -1.97710007e-01,
      -3.41470003e-01,   5.33169985e-01,  -2.53309999e-02,
       1.73800007e-01,   1.67720005e-01,   8.39839995e-01,
       5.51070012e-02,   1.05470002e-01,   3.78719985e-01,
       2.42750004e-01,   1.47449998e-02,   5.59509993e-01,
       1.25210002e-01,  -6.75960004e-01,   3.58420014e-01,
       # ... and so on ...
       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)

To make them compact and fast, spaCy's small models (all packages that end in sm) don't ship with word vectors, and only include context-sensitive tensors. This means you can still use the similarity() methods to compare documents, spans and tokens – but the result won't be as good, and individual tokens won't have any vectors assigned. So in order to use real word vectors, you need to download a larger model:

- python -m spacy download en_core_web_sm
+ python -m spacy download en_core_web_lg

Models that come with built-in word vectors make them available as the Token.vector attribute. Doc.vector and 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 normalize vectors.

### {executable="true"}
import spacy

nlp = spacy.load("en_core_web_md")
tokens = nlp("dog cat banana afskfsd")

for token in tokens:
    print(token.text, token.has_vector, token.vector_norm, token.is_oov)

• Text: The original token text.
• has vector: Does the token have a vector representation?
• Vector norm: The L2 norm of the token's vector (the square root of the sum of the values squared)
• OOV: Out-of-vocabulary

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 "afskfsd" on the other hand is a lot less common and out-of-vocabulary – so its vector representation consists of 300 dimensions of 0, which means it's practically nonexistent. If your application will benefit from a large vocabulary with more vectors, you should consider using one of the larger models or loading in a full vector package, for example, en_vectors_web_lg, which includes over 1 million unique vectors.

spaCy is able to compare two objects, and make a prediction of 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.

Each Doc, Span, Token and Lexeme comes with a .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.

### {executable="true"}
import spacy

nlp = spacy.load("en_core_web_md")  # make sure to use larger model!
tokens = nlp("dog cat banana")

for token1 in tokens:
    for token2 in tokens:
        print(token1.text, token2.text, token1.similarity(token2))

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 1.0, because of vector math and floating point imprecisions).