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92 lines
3.2 KiB
Plaintext
92 lines
3.2 KiB
Plaintext
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//- 💫 DOCS > USAGE > VECTORS & SIMILARITY > CUSTOM VECTORS
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| By default, #[+api("token#vector") #[code Token.vector]] returns the
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| vector for its underlying #[+api("lexeme") #[code Lexeme]], while
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| #[+api("doc#vector") #[code Doc.vector]] and
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| #[+api("span#vector") #[code Span.vector]] return an average of the
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| vectors of their tokens. You can customize these
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| behaviours by modifying the #[code doc.user_hooks],
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| #[code doc.user_span_hooks] and #[code doc.user_token_hooks]
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| dictionaries.
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+infobox
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| For more details on #[strong adding hooks] and #[strong overwriting] the
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| built-in #[code Doc], #[code Span] and #[code Token] methods, see the
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| usage guide on #[+a("/usage/processing-pipelines#user-hooks") user hooks].
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+h(3, "custom-vectors-add") Adding vectors
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+tag-new(2)
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p
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| The new #[+api("vectors") #[code Vectors]] class makes it easy to add
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| your own vectors to spaCy. Just like the #[+api("vocab") #[code Vocab]],
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| it is initialised with a #[+api("stringstore") #[code StringStore]] or
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| a list of strings.
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+code("Adding vectors one-by-one").
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from spacy.strings import StringStore
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from spacy.vectors import Vectors
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vector_data = {'dog': numpy.random.uniform(-1, 1, (300,)),
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'cat': numpy.random.uniform(-1, 1, (300,)),
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'orange': numpy.random.uniform(-1, 1, (300,))}
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vectors = Vectors(StringStore(), 300)
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for word, vector in vector_data.items():
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vectors.add(word, vector)
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p
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| You can also add the vector values directly on initialisation:
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+code("Adding vectors on initialisation").
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from spacy.vectors import Vectors
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vector_table = numpy.zeros((3, 300), dtype='f')
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vectors = Vectors([u'dog', u'cat', u'orange'], vector_table)
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+h(3, "custom-loading-glove") Loading GloVe vectors
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+tag-new(2)
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p
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| spaCy comes with built-in support for loading
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| #[+a("https://nlp.stanford.edu/projects/glove/") GloVe] vectors from
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| a directory. The #[+api("vectors#from_glove") #[code Vectors.from_glove]]
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| method assumes a binary format, the vocab provided in a
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| #[code vocab.txt], and the naming scheme of
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| #[code vectors.{size}.[fd].bin]. For example:
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+aside-code("Directory structure", "yaml").
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└── vectors
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├── vectors.128.f.bin # vectors file
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└── vocab.txt # vocabulary
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+table(["File name", "Dimensions", "Data type"])
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+row
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+cell #[code vectors.128.f.bin]
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+cell 128
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+cell float32
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+row
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+cell #[code vectors.300.d.bin]
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+cell 300
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+cell float64 (double)
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+code.
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from spacy.vectors import Vectors
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vectors = Vectors([], 128)
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vectors.from_glove('/path/to/vectors')
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+h(3, "custom-loading-other") Loading other vectors
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+tag-new(2)
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p
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| You can also choose to load in vectors from other sources, like the
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| #[+a("https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md") fastText vectors]
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| for 294 languages, trained on Wikipedia. After reading in the file,
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| the vectors are added to the #[code Vocab] using the
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| #[+api("vocab#set_vector") #[code set_vector]] method.
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+github("spacy", "examples/vectors_fast_text.py")
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