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364 lines
8.9 KiB
Plaintext
364 lines
8.9 KiB
Plaintext
//- 💫 DOCS > API > SPAN
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include ../../_includes/_mixins
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p A slice from a #[+api("doc") #[code Doc]] object.
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+h(2, "init") Span.__init__
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+tag method
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p Create a Span object from the #[code slice doc[start : end]].
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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span = doc[1:4]
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assert [t.text for t in span] == [u'it', u'back', u'!']
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code doc]
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+cell #[code Doc]
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+cell The parent document.
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+row
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+cell #[code start]
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+cell int
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+cell The index of the first token of the span.
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+row
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+cell #[code end]
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+cell int
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+cell The index of the first token after the span.
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+row
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+cell #[code label]
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+cell int
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+cell A label to attach to the span, e.g. for named entities.
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+row
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+cell #[code vector]
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+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
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+cell A meaning representation of the span.
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+footrow
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+cell returns
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+cell #[code Span]
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+cell The newly constructed object.
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+h(2, "getitem") Span.__getitem__
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+tag method
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p Get a #[code Token] object.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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span = doc[1:4]
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assert span[1].text == 'back'
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code i]
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+cell int
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+cell The index of the token within the span.
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+footrow
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+cell returns
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+cell #[code Token]
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+cell The token at #[code span[i]].
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p Get a #[code Span] object.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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span = doc[1:4]
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assert span[1:3].text == 'back!'
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code start_end]
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+cell tuple
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+cell The slice of the span to get.
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+footrow
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+cell returns
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+cell #[code Span]
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+cell The span at #[code span[start : end]].
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+h(2, "iter") Span.__iter__
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+tag method
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p Iterate over #[code Token] objects.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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span = doc[1:4]
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assert [t.text for t in span] == ['it', 'back', '!']
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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+cell #[code Token]
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+cell A #[code Token] object.
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+h(2, "len") Span.__len__
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+tag method
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p Get the number of tokens in the span.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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span = doc[1:4]
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assert len(span) == 3
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell int
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+cell The number of tokens in the span.
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+h(2, "similarity") Span.similarity
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+tag method
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+tag-model("vectors")
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p
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| Make a semantic similarity estimate. The default estimate is cosine
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| similarity using an average of word vectors.
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+aside-code("Example").
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doc = nlp(u'green apples and red oranges')
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green_apples = doc[:2]
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red_oranges = doc[3:]
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apples_oranges = green_apples.similarity(red_oranges)
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oranges_apples = red_oranges.similarity(green_apples)
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assert apples_oranges == oranges_apples
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code 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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+footrow
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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, "merge") Span.merge
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+tag method
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p Retokenize the document, such that the span is merged into a single token.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code **attributes]
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+cell -
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+cell
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| Attributes to assign to the merged token. By default, attributes
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| are inherited from the syntactic root token of the span.
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+footrow
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+cell returns
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+cell #[code Token]
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+cell The newly merged token.
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+h(2, "root") Span.root
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+tag property
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+tag-model("parse")
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p
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| The token within the span that's highest in the parse tree. If there's a
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| tie, the earlist is prefered.
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+aside-code("Example").
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doc = nlp(u'I like New York in Autumn.')
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i, like, new, york, in_, autumn, dot = range(len(doc))
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assert doc[new].head.text == 'York'
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assert doc[york].head.text == 'like'
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new_york = doc[new:york+1]
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assert new_york.root.text == 'York'
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell #[code Token]
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+cell The root token.
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+h(2, "lefts") Span.lefts
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+tag property
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+tag-model("parse")
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p Tokens that are to the left of the span, whose head is within the span.
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+aside-code("Example").
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doc = nlp(u'I like New York in Autumn.')
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lefts = [t.text for t in doc[3:7].lefts]
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assert lefts == [u'New']
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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+cell #[code Token]
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+cell A left-child of a token of the span.
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+h(2, "rights") Span.rights
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+tag property
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+tag-model("parse")
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p Tokens that are to the right of the span, whose head is within the span.
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+aside-code("Example").
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doc = nlp(u'I like New York in Autumn.')
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rights = [t.text for t in doc[2:4].rights]
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assert rights == [u'in']
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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+cell #[code Token]
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+cell A right-child of a token of the span.
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+h(2, "subtree") Span.subtree
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+tag property
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+tag-model("parse")
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p Tokens that descend from tokens in the span, but fall outside it.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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subtree = [t.text for t in doc[:3].subtree]
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assert subtree == [u'Give', u'it', u'back', u'!']
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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+cell #[code Token]
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+cell A descendant of a token within the span.
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+h(2, "has_vector") Span.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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| object.
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+aside-code("Example").
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doc = nlp(u'I like apples')
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assert doc[1:].has_vector
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell bool
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+cell Whether the span has a vector data attached.
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+h(2, "vector") Span.vector
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+tag property
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+tag-model("vectors")
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p
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| A real-valued meaning representation. Defaults to an average of the
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| token vectors.
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+aside-code("Example").
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doc = nlp(u'I like apples')
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assert doc[1:].vector.dtype == 'float32'
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assert doc[1:].vector.shape == (300,)
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
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+cell A 1D numpy array representing the span's semantics.
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+h(2, "vector_norm") Span.vector_norm
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+tag property
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+tag-model("vectors")
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p
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| The L2 norm of the span's vector representation.
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+aside-code("Example").
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doc = nlp(u'I like apples')
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doc[1:].vector_norm # 4.800883928527915
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doc[2:].vector_norm # 6.895897646384268
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assert doc[1:].vector_norm != doc[2:].vector_norm
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+table(["Name", "Type", "Description"])
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+footrow
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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 doc]
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+cell #[code Doc]
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+cell The parent document.
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+row
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+cell #[code sent]
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+cell #[code Span]
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+cell The sentence span that this span is a part of.
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+row
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+cell #[code start]
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+cell int
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+cell The token offset for the start of the span.
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+row
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+cell #[code end]
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+cell int
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+cell The token offset for the end of the span.
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+row
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+cell #[code start_char]
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+cell int
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+cell The character offset for the start of the span.
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+row
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+cell #[code end_char]
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+cell int
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+cell The character offset for the end of the span.
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+row
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+cell #[code text]
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+cell unicode
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+cell A unicode representation of the span text.
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+row
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+cell #[code text_with_ws]
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+cell unicode
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+cell
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| The text content of the span with a trailing whitespace character
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| if the last token has one.
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+row
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+cell #[code label]
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+cell int
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+cell The span's label.
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+row
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+cell #[code label_]
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+cell unicode
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+cell The span's label.
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+row
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+cell #[code lemma_]
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+cell unicode
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+cell The span's lemma.
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+row
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+cell #[code ent_id]
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+cell int
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+cell The hash value of the named entity the token is an instance of.
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+row
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+cell #[code ent_id_]
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+cell unicode
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+cell The string ID of the named entity the token is an instance of.
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