spaCy/website/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]
    assert [t.text for t in span] ==  [u'it', u'back', u'!']

+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.u-break numpy.ndarray[ndim=1, dtype='float32']]
        +cell A meaning representation of the span.

    +row("foot")
        +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.

    +row("foot")
        +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.

    +row("foot")
        +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]
    assert [t.text for t in span] == ['it', 'back', '!']

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

+h(2, "set_extension") Span.set_extension
    +tag classmethod
    +tag-new(2)

p
    |  Define a custom attribute on the #[code Span] which becomes available via
    |  #[code Span._]. For details, see the documentation on
    |  #[+a("/usage/processing-pipelines#custom-components-attributes") custom attributes].

+aside-code("Example").
    from spacy.tokens import Span
    city_getter = lambda span: span.text in ('New York', 'Paris', 'Berlin')
    Span.set_extension('has_city', getter=city_getter)
    doc = nlp(u'I like New York in Autumn')
    assert doc[1:4]._.has_city

+table(["Name", "Type", "Description"])
    +row
        +cell #[code name]
        +cell unicode
        +cell
            |  Name of the attribute to set by the extension. For example,
            |  #[code 'my_attr'] will be available as #[code span._.my_attr].

    +row
        +cell #[code default]
        +cell -
        +cell
            |  Optional default value of the attribute if no getter or method
            |  is defined.

    +row
        +cell #[code method]
        +cell callable
        +cell
            |  Set a custom method on the object, for example
            |  #[code span._.compare(other_span)].

    +row
        +cell #[code getter]
        +cell callable
        +cell
            |  Getter function that takes the object and returns an attribute
            |  value. Is called when the user accesses the #[code ._] attribute.

    +row
        +cell #[code setter]
        +cell callable
        +cell
            |  Setter function that takes the #[code Span] and a value, and
            |  modifies the object. Is called when the user writes to the
            |  #[code Span._] attribute.

+h(2, "get_extension") Span.get_extension
    +tag classmethod
    +tag-new(2)

p
    |  Look up a previously registered extension by name. Returns a 4-tuple
    |  #[code.u-break (default, method, getter, setter)] if the extension is
    |  registered. Raises a #[code KeyError] otherwise.

+aside-code("Example").
    from spacy.tokens import Span
    Span.set_extension('is_city', default=False)
    extension = Span.get_extension('is_city')
    assert extension == (False, None, None, None)

+table(["Name", "Type", "Description"])
    +row
        +cell #[code name]
        +cell unicode
        +cell Name of the extension.

    +row("foot")
        +cell returns
        +cell tuple
        +cell
            |  A #[code.u-break (default, method, getter, setter)] tuple of the
            |  extension.

+h(2, "has_extension") Span.has_extension
    +tag classmethod
    +tag-new(2)

p Check whether an extension has been registered on the #[code Span] class.

+aside-code("Example").
    from spacy.tokens import Span
    Span.set_extension('is_city', default=False)
    assert Span.has_extension('is_city')

+table(["Name", "Type", "Description"])
    +row
        +cell #[code name]
        +cell unicode
        +cell Name of the extension to check.

    +row("foot")
        +cell returns
        +cell bool
        +cell Whether the extension has been registered.

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

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

+aside-code("Example").
    doc = nlp(u'green apples and red oranges')
    green_apples = doc[:2]
    red_oranges = doc[3:]
    apples_oranges = green_apples.similarity(red_oranges)
    oranges_apples = red_oranges.similarity(green_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.

    +row("foot")
        +cell returns
        +cell float
        +cell A scalar similarity score. Higher is more similar.

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

p
    |  Calculates the lowest common ancestor matrix for a given #[code Span].
    |  Returns LCA matrix containing the integer index of the ancestor, or
    |  #[code -1] if no common ancestor is found, e.g. if span excludes a
    |  necessary ancestor.

+aside-code("Example").
    doc = nlp(u'I like New York in Autumn')
    span = doc[1:4]
    matrix = span.get_lca_matrix()
    # array([[0, 0, 0], [0, 1, 2], [0, 2, 2]], dtype=int32)

+table(["Name", "Type", "Description"])
    +row("foot")
        +cell returns
        +cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
        +cell The lowest common ancestor matrix of the #[code Span].


+h(2, "to_array") Span.to_array
    +tag method
    +tag-new(2)

p
    |  Given a list of #[code M] attribute IDs, export the tokens to a numpy
    |  #[code ndarray] of shape #[code (N, M)], where #[code N] is the length of
    |  the document. The values will be 32-bit integers.

+aside-code("Example").
    from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
    doc = nlp(u'I like New York in Autumn.')
    span = doc[2:3]
    # All strings mapped to integers, for easy export to numpy
    np_array = span.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])

+table(["Name", "Type", "Description"])
    +row
        +cell #[code attr_ids]
        +cell list
        +cell A list of attribute ID ints.

    +row("foot")
        +cell returns
        +cell #[code.u-break numpy.ndarray[long, ndim=2]]
        +cell
            |  A feature matrix, with one row per word, and one column per
            |  attribute indicated in the input #[code attr_ids].

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

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

+aside-code("Example").
    doc = nlp(u'I like New York in Autumn.')
    span = doc[2:4]
    span.merge()
    assert len(doc) == 6
    assert doc[2].text == 'New York'

+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.

    +row("foot")
        +cell returns
        +cell #[code Token]
        +cell The newly merged token.

+h(2, "as_doc") Span.as_doc

p
    |  Create a #[code Doc] object view of the #[code Span]'s data. Mostly
    |  useful for C-typed interfaces.

+aside-code("Example").
    doc = nlp(u'I like New York in Autumn.')
    span = doc[2:4]
    doc2 = span.as_doc()
    assert doc2.text == 'New York'

+table(["Name", "Type", "Description"])
    +row("foot")
        +cell returns
        +cell #[code Doc]
        +cell A #[code Doc] object of the #[code Span]'s content.


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

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

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

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

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

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

+aside-code("Example").
    doc = nlp(u'I like New York in Autumn.')
    lefts = [t.text for t in doc[3:7].lefts]
    assert lefts == [u'New']

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

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

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

+aside-code("Example").
    doc = nlp(u'I like New York in Autumn.')
    rights = [t.text for t in doc[2:4].rights]
    assert rights == [u'in']

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

+h(2, "n_lefts") Span.n_lefts
    +tag property
    +tag-model("parse")

p
    |  The number of tokens that are to the left of the span, whose heads are
    |  within the span.

+aside-code("Example").
    doc = nlp(u'I like New York in Autumn.')
    assert doc[3:7].n_lefts == 1

+table(["Name", "Type", "Description"])
    +row("foot")
        +cell returns
        +cell int
        +cell The number of left-child tokens.

+h(2, "n_rights") Span.n_rights
    +tag property
    +tag-model("parse")

p
    |  The number of tokens that are to the right of the span, whose heads are
    |  within the span.

+aside-code("Example").
    doc = nlp(u'I like New York in Autumn.')
    assert doc[2:4].n_rights == 1

+table(["Name", "Type", "Description"])
    +row("foot")
        +cell returns
        +cell int
        +cell The number of right-child tokens.

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

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

+aside-code("Example").
    doc = nlp(u'Give it back! He pleaded.')
    subtree = [t.text for t in doc[:3].subtree]
    assert subtree == [u'Give', u'it', u'back', u'!']

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

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

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

+aside-code("Example").
    doc = nlp(u'I like apples')
    assert doc[1:].has_vector

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

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

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

+aside-code("Example").
    doc = nlp(u'I like apples')
    assert doc[1:].vector.dtype == 'float32'
    assert doc[1:].vector.shape == (300,)

+table(["Name", "Type", "Description"])
    +row("foot")
        +cell returns
        +cell #[code.u-break 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-model("vectors")

p
    |  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]). Existst
            |  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].