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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 {title="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 [pipeline packages](/models) (all
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packages 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 pipeline package:
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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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Pipeline packages that come with built-in word vectors make them available as
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the [`Token.vector`](/api/token#vector) attribute.
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[`Doc.vector`](/api/doc#vector) and [`Span.vector`](/api/span#vector) will
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default to an average of their token vectors. You can also check if a token has
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a vector assigned, and get the L2 norm, which can be used to normalize vectors.
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```python {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("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 pipeline'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 pipeline packages or
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loading in a full vector package, for example,
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[`en_core_web_lg`](/models/en#en_core_web_lg), which includes **685k unique
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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`](/api/doc), [`Span`](/api/span), [`Token`](/api/token) and
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[`Lexeme`](/api/lexeme) comes with a [`.similarity`](/api/token#similarity)
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method that lets you compare it with another object, and determine the
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similarity. Of course similarity is always subjective – whether two words, spans
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or documents are similar really depends on how you're looking at it. spaCy's
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similarity implementation usually assumes a pretty general-purpose definition of
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similarity.
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> #### 📝 Things to try
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>
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> 1. Compare two different tokens and try to find the two most _dissimilar_
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> tokens in the texts with the lowest similarity score (according to the
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> vectors).
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> 2. Compare the similarity of two [`Lexeme`](/api/lexeme) objects, entries in
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> the vocabulary. You can get a lexeme via the `.lex` attribute of a token.
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> You should see that the similarity results are identical to the token
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> similarity.
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```python {executable="true"}
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import spacy
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nlp = spacy.load("en_core_web_md") # make sure to use larger package!
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doc1 = nlp("I like salty fries and hamburgers.")
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doc2 = nlp("Fast food tastes very good.")
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# Similarity of two documents
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print(doc1, "<->", doc2, doc1.similarity(doc2))
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# Similarity of tokens and spans
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french_fries = doc1[2:4]
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burgers = doc1[5]
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print(french_fries, "<->", burgers, french_fries.similarity(burgers))
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```
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### What to expect from similarity results {id="similarity-expectations"}
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Computing similarity scores can be helpful in many situations, but it's also
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important to maintain **realistic expectations** about what information it can
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provide. Words can be related to each other in many ways, so a single
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"similarity" score will always be a **mix of different signals**, and vectors
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trained on different data can produce very different results that may not be
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useful for your purpose. Here are some important considerations to keep in mind:
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- There's no objective definition of similarity. Whether "I like burgers" and "I
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like pasta" is similar **depends on your application**. Both talk about food
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preferences, which makes them very similar – but if you're analyzing mentions
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of food, those sentences are pretty dissimilar, because they talk about very
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different foods.
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- The similarity of [`Doc`](/api/doc) and [`Span`](/api/span) objects defaults
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to the **average** of the token vectors. This means that the vector for "fast
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food" is the average of the vectors for "fast" and "food", which isn't
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necessarily representative of the phrase "fast food".
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- Vector averaging means that the vector of multiple tokens is **insensitive to
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the order** of the words. Two documents expressing the same meaning with
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dissimilar wording will return a lower similarity score than two documents
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that happen to contain the same words while expressing different meanings.
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<Infobox title="Tip: Check out sense2vec" emoji="💡">
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<Image
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src="/images/sense2vec.jpg"
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href="https://github.com/explosion/sense2vec"
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alt="sense2vec Screenshot"
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/>
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[`sense2vec`](https://github.com/explosion/sense2vec) is a library developed by
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us that builds on top of spaCy and lets you train and query more interesting and
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detailed word vectors. It combines noun phrases like "fast food" or "fair game"
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and includes the part-of-speech tags and entity labels. The library also
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includes annotation recipes for our annotation tool [Prodigy](https://prodi.gy)
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that let you evaluate vectors and create terminology lists. For more details,
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check out [our blog post](https://explosion.ai/blog/sense2vec-reloaded). To
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explore the semantic similarities across all Reddit comments of 2015 and 2019,
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see the [interactive demo](https://explosion.ai/demos/sense2vec).
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</Infobox>
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