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Make vectors vs. tensors more explicit in 101 (see #1498)

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ines 2017-11-06 20:16:38 +01:00
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website/usage/_spacy-101/_word-vectors.jade
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@ -4,9 +4,8 @@ p
| Similarity is determined by comparing #[strong word vectors] or "word | Similarity is determined by comparing #[strong word vectors] or "word
| embeddings", multi-dimensional meaning representations of a word. Word | embeddings", multi-dimensional meaning representations of a word. Word
| vectors can be generated using an algorithm like | vectors can be generated using an algorithm like
| #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec]. spaCy's medium | #[+a("https://en.wikipedia.org/wiki/Word2vec") word2vec] and usually
| #[code md] and large #[code lg] #[+a("/models") models] come with | look like this:
| #[strong multi-dimensional vectors] that look like this:
+code("banana.vector", false, false, 250). +code("banana.vector", false, false, 250).
array([2.02280000e-01, -7.66180009e-02, 3.70319992e-01, array([2.02280000e-01, -7.66180009e-02, 3.70319992e-01,
@ -110,8 +109,21 @@ p
-2.97650009e-01, 7.89430022e-01, 3.31680000e-01, -2.97650009e-01, 7.89430022e-01, 3.31680000e-01,
-1.19659996e+00, -4.71559986e-02, 5.31750023e-01], dtype=float32) -1.19659996e+00, -4.71559986e-02, 5.31750023e-01], dtype=float32)
+infobox("Important note", "⚠️")
| To make them compact and fast, spaCy's small #[+a("/models") models]
| (all packages that end in #[code sm]) #[strong don't ship with word vectors], and
| only include context-sensitive #[strong tensors]. This means you can
| still use the #[code 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 #[em real] word vectors,
| you need to download a larger model:
+code-wrapper
+code-new(false, "bash", "$") spacy download en_core_web_lg
p p
| The #[code .vector] attribute will return an object's vector. | Models that come with built-in word vectors make them available as the
| #[+api("token#vector") #[code Token.vector]] attribute.
| #[+api("doc#vector") #[code Doc.vector]] and | #[+api("doc#vector") #[code Doc.vector]] and
| #[+api("span#vector") #[code Span.vector]] will default to an average | #[+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 | of their token vectors. You can also check if a token has a vector
@ -119,6 +131,7 @@ p
| vectors. | vectors.
+code. +code.
nlp = spacy.load('en_core_web_lg')
tokens = nlp(u'dog cat banana sasquatch') tokens = nlp(u'dog cat banana sasquatch')
for token in tokens: for token in tokens:
@ -143,10 +156,9 @@ p
| they're part of the model's vocabulary, and come with a vector. The word | 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 | "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], | – so its vector representation consists of 300 dimensions of #[code 0],
| which means it's practically nonexistent. | which means it's practically nonexistent. If your application will
| benefit from a #[strong large vocabulary] with more vectors, you should
p | consider using one of the larger models or loading in a full vector
| If your application will benefit from a large vocabulary with more | package, for example,
| vectors, you should consider using one of the | #[+a("/models/en#en_vectors_web_lg") #[code en_vectors_web_lg]], which
| #[+a("/models") larger models] instead of the default, | includes over #[strong 1 million unique vectors].
| smaller ones, which usually come with a clipped vocabulary.