spaCy/website/docs/usage/_spacy-101/_word-vectors.jade

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//- 💫 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,
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-8.53179991e-02, 1.79800004e-01, 3.38669986e-01,
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1.68530002e-01, 1.98740005e-01, -5.73849976e-01,
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2.24079996e-01, 2.39749998e-01, -6.22599982e-02,
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5.68250008e-02, -2.74210006e-01, 2.55640000e-01,
6.97930008e-02, -2.22580001e-01, -3.60060006e-01,
-2.24020004e-01, -5.36990017e-02, 1.20220006e+00,
5.45350015e-01, -5.79980016e-01, 1.09049998e-01,
4.21669990e-01, 2.06619993e-01, 1.29360005e-01,
-4.14570011e-02, -6.67770028e-01, 4.04670000e-01,
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-7.91980028e-02, 4.00369987e-02, -1.29439995e-01,
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4.19340014e-01, 1.83530003e-01, -1.52600005e-01,
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1.65189996e-01, 2.65259994e-04, 1.66160002e-01,
-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,
-8.75459984e-02, -2.39289999e-01, -2.47109994e-01,
-2.58870006e-01, -2.98940003e-01, 1.37150005e-01,
2.98919994e-02, 3.65439989e-02, -4.96650010e-01,
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-4.69460003e-02, 8.18059966e-02, 1.92789994e-02,
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3.16439986e-01, 3.75690013e-01, -7.46899992e-02,
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-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.