spaCy/website/docs/api/span.md

title	tag	source
Span	class	spacy/tokens/span.pyx

A slice from a Doc object.

Span.__init__

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

Example

doc = nlp("Give it back! He pleaded.")
span = doc[1:4]
assert [t.text for t in span] ==  ["it", "back", "!"]

Name	Description
doc	The parent document. Doc
start	The index of the first token of the span. int
end	The index of the first token after the span. int
label	A label to attach to the span, e.g. for named entities. Union[str, int]
kb_id	A knowledge base ID to attach to the span, e.g. for named entities. Union[str, int]
vector	A meaning representation of the span. numpy.ndarray[ndim=1, dtype=float32]

Span.__getitem__

Get a Token object.

Example

doc = nlp("Give it back! He pleaded.")
span = doc[1:4]
assert span[1].text == "back"

Name	Description
i	The index of the token within the span. int
RETURNS	The token at span[i]. Token

Get a Span object.

Example

doc = nlp("Give it back! He pleaded.")
span = doc[1:4]
assert span[1:3].text == "back!"

Name	Description
start_end	The slice of the span to get. Tuple[int, int]
RETURNS	The span at span[start : end]. Span

Span.__iter__

Iterate over Token objects.

Example

doc = nlp("Give it back! He pleaded.")
span = doc[1:4]
assert [t.text for t in span] == ["it", "back", "!"]

Name	Description
YIELDS	A Token object. Token

Span.__len__

Get the number of tokens in the span.

Example

doc = nlp("Give it back! He pleaded.")
span = doc[1:4]
assert len(span) == 3

Name	Description
RETURNS	The number of tokens in the span. int

Span.set_extension

Define a custom attribute on the Span which becomes available via Span._. For details, see the documentation on custom attributes.

Example

from spacy.tokens import Span
city_getter = lambda span: any(city in span.text for city in ("New York", "Paris", "Berlin"))
Span.set_extension("has_city", getter=city_getter)
doc = nlp("I like New York in Autumn")
assert doc[1:4]._.has_city

Name	Description
name	Name of the attribute to set by the extension. For example, "my_attr" will be available as span._.my_attr. str
default	Optional default value of the attribute if no getter or method is defined. Optional[Any]
method	Set a custom method on the object, for example span._.compare(other_span). Optional[CallableSpan, ...], Any
getter	Getter function that takes the object and returns an attribute value. Is called when the user accesses the ._ attribute. Optional[CallableSpan], Any
setter	Setter function that takes the Span and a value, and modifies the object. Is called when the user writes to the Span._ attribute. Optional[CallableSpan, Any], None
force	Force overwriting existing attribute. bool

Span.get_extension

Look up a previously registered extension by name. Returns a 4-tuple (default, method, getter, setter) if the extension is registered. Raises a KeyError otherwise.

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)

Name	Description
name	Name of the extension. str
RETURNS	A (default, method, getter, setter) tuple of the extension. Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]

Span.has_extension

Check whether an extension has been registered on the Span class.

Example

from spacy.tokens import Span
Span.set_extension("is_city", default=False)
assert Span.has_extension("is_city")

Name	Description
name	Name of the extension to check. str
RETURNS	Whether the extension has been registered. bool

Span.remove_extension

Remove a previously registered extension.

Example

from spacy.tokens import Span
Span.set_extension("is_city", default=False)
removed = Span.remove_extension("is_city")
assert not Span.has_extension("is_city")

Name	Description
name	Name of the extension. str
RETURNS	A (default, method, getter, setter) tuple of the removed extension. Tuple[Optional[Any], Optional[Callable], Optional[Callable], Optional[Callable]]

Span.char_span

Create a Span object from the slice span.text[start:end]. Returns None if the character indices don't map to a valid span.

Example

doc = nlp("I like New York")
span = doc[1:4].char_span(5, 13, label="GPE")
assert span.text == "New York"

Name	Description
start	The index of the first character of the span. int
end	The index of the last character after the span. int
label	A label to attach to the span, e.g. for named entities. Union[int, str]
kb_id 2.2	An ID from a knowledge base to capture the meaning of a named entity. Union[int, str]
vector	A meaning representation of the span. numpy.ndarray[ndim=1, dtype=float32]
RETURNS	The newly constructed object or None. Optional[Span]

Span.similarity

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

Example

doc = nlp("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

Name	Description
other	The object to compare with. By default, accepts Doc, Span, Token and Lexeme objects. Union[Doc, Span, Token, Lexeme]
RETURNS	A scalar similarity score. Higher is more similar. float

Span.get_lca_matrix

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

Example

doc = nlp("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)

Name	Description
RETURNS	The lowest common ancestor matrix of the Span. numpy.ndarray[ndim=2, dtype=int32]

Span.to_array

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

Example

from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
doc = nlp("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])

Name	Description
attr_ids	A list of attributes (int IDs or string names) or a single attribute (int ID or string name). Union[int, str, List[Union[int, str]]]
RETURNS	The exported attributes as a numpy array. Union[numpy.ndarray[ndim=2, dtype=uint64], numpy.ndarray[ndim=1, dtype=uint64]]

Span.ents

The named entities in the span. Returns a tuple of named entity Span objects, if the entity recognizer has been applied.

Example

doc = nlp("Mr. Best flew to New York on Saturday morning.")
span = doc[0:6]
ents = list(span.ents)
assert ents[0].label == 346
assert ents[0].label_ == "PERSON"
assert ents[0].text == "Mr. Best"

Name	Description
RETURNS	Entities in the span, one Span per entity. Tuple[Span, ...]

Span.noun_chunks

Iterate over the base noun phrases in the span. Yields base noun-phrase Span objects, if the document has been syntactically parsed. A base noun phrase, or "NP chunk", is a noun phrase that does not permit other NPs to be nested within it – so no NP-level coordination, no prepositional phrases, and no relative clauses.

If the noun_chunk syntax iterator has not been implemeted for the given language, a NotImplementedError is raised.

Example

doc = nlp("A phrase with another phrase occurs.")
span = doc[3:5]
chunks = list(span.noun_chunks)
assert len(chunks) == 1
assert chunks[0].text == "another phrase"

Name	Description
YIELDS	Noun chunks in the span. Span

Span.as_doc

Create a new Doc object corresponding to the Span, with a copy of the data.

Example

doc = nlp("I like New York in Autumn.")
span = doc[2:4]
doc2 = span.as_doc()
assert doc2.text == "New York"

Name	Description
copy_user_data	Whether or not to copy the original doc's user data. bool
RETURNS	A Doc object of the Span's content. Doc

Span.root

The token with the shortest path to the root of the sentence (or the root itself). If multiple tokens are equally high in the tree, the first token is taken.

Example

doc = nlp("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"

Name	Description
RETURNS	The root token. Token

Span.conjuncts

A tuple of tokens coordinated to span.root.

Example

doc = nlp("I like apples and oranges")
apples_conjuncts = doc[2:3].conjuncts
assert [t.text for t in apples_conjuncts] == ["oranges"]

Name	Description
RETURNS	The coordinated tokens. Tuple[Token, ...]

Span.lefts

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

Example

doc = nlp("I like New York in Autumn.")
lefts = [t.text for t in doc[3:7].lefts]
assert lefts == ["New"]

Name	Description
YIELDS	A left-child of a token of the span. Token

Span.rights

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

Example

doc = nlp("I like New York in Autumn.")
rights = [t.text for t in doc[2:4].rights]
assert rights == ["in"]

Name	Description
YIELDS	A right-child of a token of the span. Token

Span.n_lefts

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

Example

doc = nlp("I like New York in Autumn.")
assert doc[3:7].n_lefts == 1

Name	Description
RETURNS	The number of left-child tokens. int

Span.n_rights

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

Example

doc = nlp("I like New York in Autumn.")
assert doc[2:4].n_rights == 1

Name	Description
RETURNS	The number of right-child tokens. int

Span.subtree

Tokens within the span and tokens which descend from them.

Example

doc = nlp("Give it back! He pleaded.")
subtree = [t.text for t in doc[:3].subtree]
assert subtree == ["Give", "it", "back", "!"]

Name	Description
YIELDS	A token within the span, or a descendant from it. Token

Span.has_vector

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

Example

doc = nlp("I like apples")
assert doc[1:].has_vector

Name	Description
RETURNS	Whether the span has a vector data attached. bool

Span.vector

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

Example

doc = nlp("I like apples")
assert doc[1:].vector.dtype == "float32"
assert doc[1:].vector.shape == (300,)

Name	Description
RETURNS	A 1-dimensional array representing the span's vector. `numpy.ndarray[ndim=1, dtype=float32]

Span.vector_norm

The L2 norm of the span's vector representation.

Example

doc = nlp("I like apples")
doc[1:].vector_norm # 4.800883928527915
doc[2:].vector_norm # 6.895897646384268
assert doc[1:].vector_norm != doc[2:].vector_norm

Name	Description
RETURNS	The L2 norm of the vector representation. float

Span.sent

The sentence span that this span is a part of. This property is only available when sentence boundaries have been set on the document by the parser, senter, sentencizer or some custom function. It will raise an error otherwise.

If the span happens to cross sentence boundaries, only the first sentence will be returned. If it is required that the sentence always includes the full span, the result can be adjusted as such:

sent = span.sent
sent = doc[sent.start : max(sent.end, span.end)]

Example

doc = nlp("Give it back! He pleaded.")
span = doc[1:3]
assert span.sent.text == "Give it back!"

Name	Description
RETURNS	The sentence span that this span is a part of. Span

Attributes

Name	Description
doc	The parent document. Doc
tensor 2.1.7	The span's slice of the parent Doc's tensor. numpy.ndarray
start	The token offset for the start of the span. int
end	The token offset for the end of the span. int
start_char	The character offset for the start of the span. int
end_char	The character offset for the end of the span. int
text	A string representation of the span text. str
text_with_ws	The text content of the span with a trailing whitespace character if the last token has one. str
orth	ID of the verbatim text content. int
orth_	Verbatim text content (identical to Span.text). Exists mostly for consistency with the other attributes. str
label	The hash value of the span's label. int
label_	The span's label. str
lemma_	The span's lemma. Equivalent to "".join(token.text_with_ws for token in span). str
kb_id	The hash value of the knowledge base ID referred to by the span. int
kb_id_	The knowledge base ID referred to by the span. str
ent_id	The hash value of the named entity the token is an instance of. int
ent_id_	The string ID of the named entity the token is an instance of. str
sentiment	A scalar value indicating the positivity or negativity of the span. float
_	User space for adding custom attribute extensions. Underscore