mirror of https://github.com/explosion/spaCy.git
302 lines
7.5 KiB
Plaintext
302 lines
7.5 KiB
Plaintext
//- 💫 DOCS > API > LEXEME
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include ../_includes/_mixins
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p
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| An entry in the vocabulary. A #[code Lexeme] has no string context – it's
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| a word type, as opposed to a word token. It therefore has no
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| part-of-speech tag, dependency parse, or lemma (if lemmatization depends
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| on the part-of-speech tag).
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+h(2, "init") Lexeme.__init__
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+tag method
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p Create a #[code Lexeme] object.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell The parent vocabulary.
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+row
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+cell #[code orth]
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+cell int
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+cell The orth id of the lexeme.
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+row("foot")
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+cell returns
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+cell #[code Lexeme]
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+cell The newly constructed object.
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+h(2, "set_flag") Lexeme.set_flag
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+tag method
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p Change the value of a boolean flag.
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+aside-code("Example").
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COOL_FLAG = nlp.vocab.add_flag(lambda text: False)
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nlp.vocab[u'spaCy'].set_flag(COOL_FLAG, True)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to set.
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+row
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+cell #[code value]
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+cell bool
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+cell The new value of the flag.
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+h(2, "check_flag") Lexeme.check_flag
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+tag method
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p Check the value of a boolean flag.
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+aside-code("Example").
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is_my_library = lambda text: text in ['spaCy', 'Thinc']
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MY_LIBRARY = nlp.vocab.add_flag(is_my_library)
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assert nlp.vocab[u'spaCy'].check_flag(MY_LIBRARY) == True
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to query.
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+row("foot")
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+cell returns
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+cell bool
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+cell The value of the flag.
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+h(2, "similarity") Lexeme.similarity
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+tag method
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+tag-model("vectors")
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p Compute a semantic similarity estimate. Defaults to cosine over vectors.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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orange = nlp.vocab[u'orange']
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apple_orange = apple.similarity(orange)
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orange_apple = orange.similarity(apple)
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assert apple_orange == orange_apple
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+table(["Name", "Type", "Description"])
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+row
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+cell other
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+cell -
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+cell
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| The object to compare with. By default, accepts #[code Doc],
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| #[code Span], #[code Token] and #[code Lexeme] objects.
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+row("foot")
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+cell returns
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+cell float
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+cell A scalar similarity score. Higher is more similar.
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+h(2, "has_vector") Lexeme.has_vector
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+tag property
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+tag-model("vectors")
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p
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| A boolean value indicating whether a word vector is associated with the
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| lexeme.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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assert apple.has_vector
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell returns
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+cell bool
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+cell Whether the lexeme has a vector data attached.
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+h(2, "vector") Lexeme.vector
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+tag property
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+tag-model("vectors")
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p A real-valued meaning representation.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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assert apple.vector.dtype == 'float32'
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assert apple.vector.shape == (300,)
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell returns
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell A 1D numpy array representing the lexeme's semantics.
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+h(2, "vector_norm") Lexeme.vector_norm
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+tag property
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+tag-model("vectors")
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p The L2 norm of the lexeme's vector representation.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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pasta = nlp.vocab[u'pasta']
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apple.vector_norm # 7.1346845626831055
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pasta.vector_norm # 7.759851932525635
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assert apple.vector_norm != pasta.vector_norm
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell returns
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+cell float
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+cell The L2 norm of the vector representation.
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+h(2, "attributes") Attributes
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell
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+row
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+cell #[code text]
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+cell unicode
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+cell Verbatim text content.
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+row
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+cell #[code lex_id]
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+cell int
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+cell ID of the lexeme's lexical type.
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+row
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+cell #[code lower]
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+cell int
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+cell Lower-case form of the word.
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+row
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+cell #[code lower_]
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+cell unicode
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+cell Lower-case form of the word.
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+row
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+cell #[code shape]
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+cell int
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+cell Transform of the word's string, to show orthographic features.
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+row
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+cell #[code shape_]
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+cell unicode
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+cell Transform of the word's string, to show orthographic features.
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+row
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+cell #[code prefix]
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+cell int
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+cell Length-N substring from the start of the word. Defaults to #[code N=1].
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+row
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+cell #[code prefix_]
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+cell unicode
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+cell Length-N substring from the start of the word. Defaults to #[code N=1].
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+row
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+cell #[code suffix]
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+cell int
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+cell Length-N substring from the end of the word. Defaults to #[code N=3].
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+row
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+cell #[code suffix_]
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+cell unicode
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+cell Length-N substring from the start of the word. Defaults to #[code N=3].
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+row
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+cell #[code is_alpha]
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+cell bool
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+cell
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| Does the lexeme consist of alphabetic characters? Equivalent to
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| #[code lexeme.text.isalpha()].
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+row
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+cell #[code is_ascii]
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+cell bool
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+cell
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| Does the lexeme consist of ASCII characters? Equivalent to
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| #[code [any(ord(c) >= 128 for c in lexeme.text)]].
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+row
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+cell #[code is_digit]
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+cell bool
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+cell
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| Does the lexeme consist of digits? Equivalent to
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| #[code lexeme.text.isdigit()].
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+row
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+cell #[code is_lower]
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+cell bool
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+cell
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| Is the lexeme in lowercase? Equivalent to
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| #[code lexeme.text.islower()].
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+row
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+cell #[code is_title]
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+cell bool
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+cell
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| Is the lexeme in titlecase? Equivalent to
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| #[code lexeme.text.istitle()].
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+row
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+cell #[code is_punct]
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+cell bool
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+cell Is the lexeme punctuation?
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+row
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+cell #[code is_space]
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+cell bool
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+cell
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| Does the lexeme consist of whitespace characters? Equivalent to
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| #[code lexeme.text.isspace()].
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+row
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+cell #[code like_url]
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+cell bool
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+cell Does the lexeme resemble a URL?
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+row
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+cell #[code like_num]
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+cell bool
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+cell Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc.
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+row
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+cell #[code like_email]
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+cell bool
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+cell Does the lexeme resemble an email address?
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+row
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+cell #[code is_oov]
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+cell bool
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+cell Is the lexeme out-of-vocabulary?
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+row
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+cell #[code is_stop]
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+cell bool
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+cell Is the lexeme part of a "stop list"?
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+row
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+cell #[code lang]
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+cell int
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+cell Language of the parent vocabulary.
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+row
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+cell #[code lang_]
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+cell unicode
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+cell Language of the parent vocabulary.
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+row
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+cell #[code prob]
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+cell float
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+cell Smoothed log probability estimate of lexeme's type.
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+row
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+cell #[code sentiment]
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+cell float
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+cell A scalar value indicating the positivity or negativity of the lexeme.
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