spaCy/website/docs/api/span.jade

//- 💫 DOCS > API > SPAN

include ../../_includes/_mixins

p A slice from a #[+api("doc") #[code Doc]] object.

+h(2, "init") Span.__init__
    +tag method

p Create a Span object from the #[code slice doc[start : end]].

+aside-code("Example").
    doc = nlp(u'Give it back! He pleaded.')
    span = doc[1:4]
    print([token.text for token in span])
    # ['it', 'back', '!']

+table(["Name", "Type", "Description"])
    +row
        +cell #[code doc]
        +cell #[code Doc]
        +cell The parent document.

    +row
        +cell #[code start]
        +cell int
        +cell The index of the first token of the span.

    +row
        +cell #[code end]
        +cell int
        +cell The index of the first token after the span.

    +row
        +cell #[code label]
        +cell int
        +cell A label to attach to the span, e.g. for named entities.

    +row
        +cell #[code vector]
        +cell #[code numpy.ndarray[ndim=1, dtype='float32']]
        +cell A meaning representation of the span.

    +footrow
        +cell returns
        +cell #[code Span]
        +cell The newly constructed object.

+h(2, "getitem") Span.__getitem__
    +tag method

p Get a #[code Token] object.

+aside-code("Example").
    doc = nlp(u'Give it back! He pleaded.')
    span = doc[1:4]
    assert span[1].text == 'back'

+table(["Name", "Type", "Description"])
    +row
        +cell #[code i]
        +cell int
        +cell The index of the token within the span.

    +footrow
        +cell returns
        +cell #[code Token]
        +cell The token at #[code span[i]].

p Get a #[code Span] object.

+aside-code("Example").
    doc = nlp(u'Give it back! He pleaded.')
    span = doc[1:4]
    assert span[1:3].text == 'back!'

+table(["Name", "Type", "Description"])
    +row
        +cell #[code start_end]
        +cell tuple
        +cell The slice of the span to get.

    +footrow
        +cell returns
        +cell #[code Span]
        +cell The span at #[code span[start : end]].

+h(2, "iter") Span.__iter__
    +tag method

p Iterate over #[code Token] objects.

+aside-code("Example").
    doc = nlp(u'Give it back! He pleaded.')
    span = doc[1:4]
    print([token.text for token in span])
    # ['it', 'back', '!']

+table(["Name", "Type", "Description"])
    +footrow
        +cell yields
        +cell #[code Token]
        +cell A #[code Token] object.

+h(2, "len") Span.__len__
    +tag method

p Get the number of tokens in the span.

+aside-code("Example").
    doc = nlp(u'Give it back! He pleaded.')
    span = doc[1:4]
    assert len(span) == 3

+table(["Name", "Type", "Description"])
    +footrow
        +cell returns
        +cell int
        +cell The number of tokens in the span.

+h(2, "similarity") Span.similarity
    +tag method
    +tag requires model

p
    |  Make a semantic similarity estimate. The default estimate is cosine
    |  similarity using an average of word vectors.

+aside-code("Example").
    apples, and, oranges = nlp(u'apples and oranges')
    apples_oranges = apples.similarity(oranges)
    oranges_apples = oranges.similarity(apples)
    assert apples_oranges == oranges_apples

+table(["Name", "Type", "Description"])
    +row
        +cell #[code other]
        +cell -
        +cell
            |  The object to compare with. By default, accepts #[code Doc],
            |  #[code Span], #[code Token] and #[code Lexeme] objects.

    +footrow
        +cell returns
        +cell float
        +cell A scalar similarity score. Higher is more similar.

+h(2, "merge") Span.merge
    +tag method

p Retokenize the document, such that the span is merged into a single token.

+table(["Name", "Type", "Description"])
    +row
        +cell #[code **attributes]
        +cell -
        +cell
            |  Attributes to assign to the merged token. By default, attributes
            |  are inherited from the syntactic root token of the span.

    +footrow
        +cell returns
        +cell #[code Token]
        +cell The newly merged token.

+h(2, "root") Span.root
    +tag property

p
    |  The token within the span that's highest in the parse tree. If there's a
    |  tie, the earlist is prefered.

+aside-code("Example").
    tokens = nlp(u'I like New York in Autumn.')
    i, like, new, york, in_, autumn, dot = range(len(tokens))
    assert tokens[new].head.text == 'York'
    assert tokens[york].head.text == 'like'
    new_york = tokens[new:york+1]
    assert new_york.root.text == 'York'

+table(["Name", "Type", "Description"])
    +footrow
        +cell returns
        +cell #[code Token]
        +cell The root token.

+h(2, "lefts") Span.lefts
    +tag property

p Tokens that are to the left of the span, whose head is within the span.

+table(["Name", "Type", "Description"])
    +footrow
        +cell yields
        +cell #[code Token]
        +cell A left-child of a token of the span.

+h(2, "rights") Span.rights
    +tag property

p Tokens that are to the right of the span, whose head is within the span.

+table(["Name", "Type", "Description"])
    +footrow
        +cell yields
        +cell #[code Token]
        +cell A right-child of a token of the span.

+h(2, "subtree") Span.subtree
    +tag property

p Tokens that descend from tokens in the span, but fall outside it.

+table(["Name", "Type", "Description"])
    +footrow
        +cell yields
        +cell #[code Token]
        +cell A descendant of a token within the span.

+h(2, "has_vector") Span.has_vector
    +tag property
    +tag requires model

p
    |  A boolean value indicating whether a word vector is associated with the
    |  object.

+aside-code("Example").
    apple = nlp(u'apple')
    assert apple.has_vector

+table(["Name", "Type", "Description"])
    +footrow
        +cell returns
        +cell bool
        +cell Whether the span has a vector data attached.

+h(2, "vector") Span.vector
    +tag property
    +tag requires model

p
    |  A real-valued meaning representation. Defaults to an average of the
    |  token vectors.

+aside-code("Example").
    apple = nlp(u'apple')
    (apple.vector.dtype, apple.vector.shape)
    # (dtype('float32'), (300,))

+table(["Name", "Type", "Description"])
    +footrow
        +cell returns
        +cell #[code numpy.ndarray[ndim=1, dtype='float32']]
        +cell A 1D numpy array representing the span's semantics.

+h(2, "vector_norm") Span.vector_norm
    +tag property
    +tag requires model

p
    |  The L2 norm of the span's vector representation.

+table(["Name", "Type", "Description"])
    +footrow
        +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 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 integer ID 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.