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656 lines
17 KiB
Plaintext
656 lines
17 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.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell A meaning representation of the span.
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+row("foot")
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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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+row("foot")
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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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+row("foot")
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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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+row("foot")
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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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+row("foot")
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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, "set_extension") Span.set_extension
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+tag classmethod
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+tag-new(2)
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p
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| Define a custom attribute on the #[code Span] which becomes available via
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| #[code Span._]. For details, see the documentation on
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| #[+a("/usage/processing-pipelines#custom-components-attributes") custom attributes].
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+aside-code("Example").
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from spacy.tokens import Span
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city_getter = lambda span: any(city in span.text for city in ('New York', 'Paris', 'Berlin'))
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Span.set_extension('has_city', getter=city_getter)
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doc = nlp(u'I like New York in Autumn')
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assert doc[1:4]._.has_city
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code name]
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+cell unicode
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+cell
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| Name of the attribute to set by the extension. For example,
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| #[code 'my_attr'] will be available as #[code span._.my_attr].
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+row
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+cell #[code default]
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+cell -
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+cell
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| Optional default value of the attribute if no getter or method
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| is defined.
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+row
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+cell #[code method]
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+cell callable
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+cell
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| Set a custom method on the object, for example
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| #[code span._.compare(other_span)].
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+row
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+cell #[code getter]
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+cell callable
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+cell
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| Getter function that takes the object and returns an attribute
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| value. Is called when the user accesses the #[code ._] attribute.
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+row
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+cell #[code setter]
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+cell callable
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+cell
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| Setter function that takes the #[code Span] and a value, and
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| modifies the object. Is called when the user writes to the
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| #[code Span._] attribute.
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+h(2, "get_extension") Span.get_extension
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+tag classmethod
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+tag-new(2)
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p
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| Look up a previously registered extension by name. Returns a 4-tuple
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| #[code.u-break (default, method, getter, setter)] if the extension is
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| registered. Raises a #[code KeyError] otherwise.
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+aside-code("Example").
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from spacy.tokens import Span
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Span.set_extension('is_city', default=False)
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extension = Span.get_extension('is_city')
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assert extension == (False, None, None, None)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code name]
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+cell unicode
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+cell Name of the extension.
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+row("foot")
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+cell returns
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+cell tuple
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+cell
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| A #[code.u-break (default, method, getter, setter)] tuple of the
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| extension.
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+h(2, "has_extension") Span.has_extension
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+tag classmethod
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+tag-new(2)
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p Check whether an extension has been registered on the #[code Span] class.
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+aside-code("Example").
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from spacy.tokens import Span
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Span.set_extension('is_city', default=False)
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assert Span.has_extension('is_city')
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code name]
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+cell unicode
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+cell Name of the extension to check.
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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 extension has been registered.
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+h(2, "remove_extension") Span.remove_extension
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+tag classmethod
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+tag-new("2.0.12")
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p Remove a previously registered extension.
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+aside-code("Example").
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from spacy.tokens import Span
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Span.set_extension('is_city', default=False)
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removed = Span.remove_extension('is_city')
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assert not Span.has_extension('is_city')
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code name]
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+cell unicode
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+cell Name of the extension.
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+row("foot")
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+cell returns
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+cell tuple
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+cell
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| A #[code.u-break (default, method, getter, setter)] tuple of the
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| removed extension.
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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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+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, "get_lca_matrix") Span.get_lca_matrix
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+tag method
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p
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| Calculates the lowest common ancestor matrix for a given #[code Span].
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| Returns LCA matrix containing the integer index of the ancestor, or
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| #[code -1] if no common ancestor is found, e.g. if span excludes a
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| necessary ancestor.
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+aside-code("Example").
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doc = nlp(u'I like New York in Autumn')
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span = doc[1:4]
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matrix = span.get_lca_matrix()
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# array([[0, 0, 0], [0, 1, 2], [0, 2, 2]], dtype=int32)
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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=2, dtype='int32']]
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+cell The lowest common ancestor matrix of the #[code Span].
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+h(2, "to_array") Span.to_array
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+tag method
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+tag-new(2)
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p
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| Given a list of #[code M] attribute IDs, export the tokens to a numpy
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| #[code ndarray] of shape #[code (N, M)], where #[code N] is the length of
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| the document. The values will be 32-bit integers.
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+aside-code("Example").
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from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
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doc = nlp(u'I like New York in Autumn.')
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span = doc[2:3]
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# All strings mapped to integers, for easy export to numpy
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np_array = span.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code attr_ids]
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+cell list
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+cell A list of attribute ID ints.
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+row("foot")
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+cell returns
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+cell #[code.u-break numpy.ndarray[long, ndim=2]]
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+cell
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| A feature matrix, with one row per word, and one column per
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| attribute indicated in the input #[code attr_ids].
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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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+aside-code("Example").
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doc = nlp(u'I like New York in Autumn.')
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span = doc[2:4]
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span.merge()
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assert len(doc) == 6
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assert doc[2].text == 'New York'
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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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+row("foot")
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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, "ents") Span.ents
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+tag property
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+tag-model("NER")
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+tag-new("2.0.12")
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p
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| Iterate over the entities in the span. Yields named-entity
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| #[code Span] objects, if the entity recognizer has been applied to the
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| parent document.
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+aside-code("Example").
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doc = nlp(u'Mr. Best flew to New York on Saturday morning.')
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span = doc[0:6]
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ents = list(span.ents)
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assert ents[0].label == 346
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assert ents[0].label_ == 'PERSON'
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assert ents[0].text == 'Mr. Best'
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell yields
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+cell #[code Span]
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+cell Entities in the document.
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+h(2, "as_doc") Span.as_doc
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p
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| Create a new #[code Doc] object corresponding to the #[code Span], with
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| a copy of the data.
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+aside-code("Example").
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doc = nlp(u'I like New York in Autumn.')
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span = doc[2:4]
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doc2 = span.as_doc()
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assert doc2.text == 'New York'
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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 Doc]
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+cell A #[code Doc] object of the #[code Span]'s content.
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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 earliest is preferred.
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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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+row("foot")
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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 heads are 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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+row("foot")
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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 heads are 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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+row("foot")
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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, "n_lefts") Span.n_lefts
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+tag property
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+tag-model("parse")
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p
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| The number of tokens that are to the left of the span, whose heads are
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| 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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assert doc[3:7].n_lefts == 1
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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 int
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+cell The number of left-child tokens.
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+h(2, "n_rights") Span.n_rights
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+tag property
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+tag-model("parse")
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p
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| The number of tokens that are to the right of the span, whose heads are
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| 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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assert doc[2:4].n_rights == 1
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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 int
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+cell The number of right-child tokens.
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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 within the span and tokens which descend from them.
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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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+row("foot")
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+cell yields
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+cell #[code Token]
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+cell A token within the span, or a descendant from it.
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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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+row("foot")
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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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+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 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.
|
|
|
|
+aside-code("Example").
|
|
doc = nlp(u'I like apples')
|
|
doc[1:].vector_norm # 4.800883928527915
|
|
doc[2:].vector_norm # 6.895897646384268
|
|
assert doc[1:].vector_norm != doc[2:].vector_norm
|
|
|
|
+table(["Name", "Type", "Description"])
|
|
+row("foot")
|
|
+cell returns
|
|
+cell float
|
|
+cell The L2 norm of the vector representation.
|
|
|
|
+h(2, "attributes") Attributes
|
|
|
|
+table(["Name", "Type", "Description"])
|
|
+row
|
|
+cell #[code doc]
|
|
+cell #[code Doc]
|
|
+cell The parent document.
|
|
|
|
+row
|
|
+cell #[code sent]
|
|
+cell #[code Span]
|
|
+cell The sentence span that this span is a part of.
|
|
|
|
+row
|
|
+cell #[code start]
|
|
+cell int
|
|
+cell The token offset for the start of the span.
|
|
|
|
+row
|
|
+cell #[code end]
|
|
+cell int
|
|
+cell The token offset for the end of the span.
|
|
|
|
+row
|
|
+cell #[code start_char]
|
|
+cell int
|
|
+cell The character offset for the start of the span.
|
|
|
|
+row
|
|
+cell #[code end_char]
|
|
+cell int
|
|
+cell The character offset for the end of the span.
|
|
|
|
+row
|
|
+cell #[code text]
|
|
+cell unicode
|
|
+cell A unicode representation of the span text.
|
|
|
|
+row
|
|
+cell #[code text_with_ws]
|
|
+cell unicode
|
|
+cell
|
|
| The text content of the span with a trailing whitespace character
|
|
| if the last token has one.
|
|
|
|
+row
|
|
+cell #[code orth]
|
|
+cell int
|
|
+cell ID of the verbatim text content.
|
|
|
|
+row
|
|
+cell #[code orth_]
|
|
+cell unicode
|
|
+cell
|
|
| Verbatim text content (identical to #[code Span.text]). Exists
|
|
| mostly for consistency with the other attributes.
|
|
|
|
+row
|
|
+cell #[code label]
|
|
+cell int
|
|
+cell The span's label.
|
|
|
|
+row
|
|
+cell #[code label_]
|
|
+cell unicode
|
|
+cell The span's label.
|
|
|
|
+row
|
|
+cell #[code lemma_]
|
|
+cell unicode
|
|
+cell The span's lemma.
|
|
|
|
+row
|
|
+cell #[code ent_id]
|
|
+cell int
|
|
+cell The hash value of the named entity the token is an instance of.
|
|
|
|
+row
|
|
+cell #[code ent_id_]
|
|
+cell unicode
|
|
+cell The string ID of the named entity the token is an instance of.
|
|
|
|
+row
|
|
+cell #[code sentiment]
|
|
+cell float
|
|
+cell
|
|
| A scalar value indicating the positivity or negativity of the
|
|
| span.
|
|
|
|
+row
|
|
+cell #[code _]
|
|
+cell #[code Underscore]
|
|
+cell
|
|
| User space for adding custom
|
|
| #[+a("/usage/processing-pipelines#custom-components-attributes") attribute extensions].
|