//- 💫 DOCS > USAGE > SPACY 101 > WORD VECTORS

p
    |  Similarity is determined by comparing #[strong word vectors] or "word
    |  embeddings", multi-dimensional meaning representations of a word. Word
    |  vectors can be generated using an algorithm like
    |  #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]. Most of spaCy's
    |  #[+a("/docs/usage/models") default models] come with
    |  #[strong 300-dimensional vectors] that look like this:

+code("banana.vector", false, false, 250).
    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,
          -4.00279984e-02,   9.59490016e-02,  -5.06900012e-01,
          -8.53179991e-02,   1.79800004e-01,   3.38669986e-01,
           1.32300004e-01,   3.10209990e-01,   2.18779996e-01,
           1.68530002e-01,   1.98740005e-01,  -5.73849976e-01,
          -1.06490001e-01,   2.66689986e-01,   1.28380001e-01,
          -1.28030002e-01,  -1.32839993e-01,   1.26570001e-01,
           8.67229998e-01,   9.67210010e-02,   4.83060002e-01,
           2.12709993e-01,  -5.49900010e-02,  -8.24249983e-02,
           2.24079996e-01,   2.39749998e-01,  -6.22599982e-02,
           6.21940017e-01,  -5.98999977e-01,   4.32009995e-01,
           2.81430006e-01,   3.38420011e-02,  -4.88150001e-01,
          -2.13589996e-01,   2.74010003e-01,   2.40950003e-01,
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           4.01950002e-01,  -1.87680006e-01,   5.16870022e-01,
           1.25200003e-01,   8.41499984e-01,   1.20970003e-01,
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           5.68250008e-02,  -2.74210006e-01,   2.55640000e-01,
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           4.19340014e-01,   1.83530003e-01,  -1.52600005e-01,
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          -1.52810007e-01,   1.81229994e-01,   7.02740014e-01,
           5.79559989e-03,   5.16639985e-02,  -5.97449988e-02,
          -2.75510013e-01,  -3.90489995e-01,   6.11319989e-02,
           5.54300010e-01,  -8.79969969e-02,  -4.16810006e-01,
           3.28260005e-01,  -5.25489986e-01,  -4.42880005e-01,
           8.21829960e-03,   2.44859993e-01,  -2.29819998e-01,
          -3.49810004e-01,   2.68940002e-01,   3.91660005e-01,
          -4.19039994e-01,   1.61909997e-01,  -2.62630010e+00,
           6.41340017e-01,   3.97430003e-01,  -1.28680006e-01,
          -3.19460005e-01,  -2.56330013e-01,  -1.22199997e-01,
           3.22750002e-01,  -7.99330026e-02,  -1.53479993e-01,
           3.15050006e-01,   3.05909991e-01,   2.60120004e-01,
           1.85530007e-01,  -2.40429997e-01,   4.28860001e-02,
           4.06219989e-01,  -2.42559999e-01,   6.38700008e-01,
           6.99829996e-01,  -1.40430003e-01,   2.52090007e-01,
           4.89840001e-01,  -6.10670000e-02,  -3.67659986e-01,
          -5.50890028e-01,  -3.82649988e-01,  -2.08430007e-01,
           2.28320003e-01,   5.12179971e-01,   2.78679997e-01,
           4.76520002e-01,   4.79510017e-02,  -3.40079993e-01,
          -3.28729987e-01,  -4.19669986e-01,  -7.54989982e-02,
          -3.89539987e-01,  -2.96219997e-02,  -3.40700001e-01,
           2.21699998e-01,  -6.28560036e-02,  -5.19029975e-01,
          -3.77739996e-01,  -4.34770016e-03,  -5.83010018e-01,
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          -2.75870003e-02,  -1.00000000e+00,   1.83870003e-01,
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          -9.19600017e-03,  -1.92950002e-03,   2.81890005e-01,
           2.72470005e-02,   4.34089988e-01,  -5.49669981e-01,
          -9.74259973e-02,  -2.45399997e-01,  -1.72030002e-01,
          -8.86500031e-02,  -3.02980006e-01,  -1.35910004e-01,
          -2.77649999e-01,   3.12860007e-03,   2.05559999e-01,
          -1.57720000e-01,  -5.23079991e-01,  -6.47010028e-01,
          -3.70139986e-01,   6.93930015e-02,   1.14009999e-01,
           2.75940001e-01,  -1.38750002e-01,  -2.72680014e-01,
           6.68910027e-01,  -5.64539991e-02,   2.40170002e-01,
          -2.67300010e-01,   2.98599988e-01,   1.00830004e-01,
           5.55920005e-01,   3.28489989e-01,   7.68579990e-02,
           1.55279994e-01,   2.56359994e-01,  -1.07720003e-01,
          -1.23590000e-01,   1.18270002e-01,  -9.90289971e-02,
          -3.43279988e-01,   1.15019999e-01,  -3.78080010e-01,
          -3.90120000e-02,  -3.45930010e-01,  -1.94040000e-01,
          -3.35799992e-01,  -6.23340011e-02,   2.89189994e-01,
           2.80319989e-01,  -5.37410021e-01,   6.27939999e-01,
           5.69549985e-02,   6.21469975e-01,  -2.52819985e-01,
           4.16700006e-01,  -1.01079997e-02,  -2.54339993e-01,
           4.00029987e-01,   4.24320012e-01,   2.26720005e-01,
           1.75530002e-01,   2.30489999e-01,   2.83230007e-01,
           1.38820007e-01,   3.12180002e-03,   1.70570001e-01,
           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)

p
    |  The #[code .vector] attribute will return an object's vector.
    |  #[+api("doc#vector") #[code Doc.vector]] and
    |  #[+api("span#vector") #[code 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 normalise
    |  vectors.

+code.
    tokens = nlp(u'dog cat banana sasquatch')

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

+aside
    |  #[strong Text]: The original token text.#[br]
    |  #[strong has vector]: Does the token have a vector representation?#[br]
    |  #[strong Vector norm]: The L2 norm of the token's vector (the square root
    |  of the sum of the values squared)#[br]
    |  #[strong is OOV]: Is the word out-of-vocabulary?

+table(["Text", "Has vector", "Vector norm", "OOV"])
    - var style = [0, 1, 1, 1]
    +annotation-row(["dog", true, 7.033672992262838, false], style)
    +annotation-row(["cat", true, 6.68081871208896, false], style)
    +annotation-row(["banana", true, 6.700014292148571, false], style)
    +annotation-row(["sasquatch", false, 0, true], style)

p
    |  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
    |  "sasquatch" on the other hand is a lot less common and out-of-vocabulary
    |  – so its vector representation consists of 300 dimensions of #[code 0],
    |  which means it's practically nonexistent.

p
    |  If your application will benefit from a large vocabulary with more
    |  vectors, you should consider using one of the
    |  #[+a("/docs/usage/models#available") larger models] instead of the default,
    |  smaller ones, which usually come with a clipped vocabulary.