spaCy/website/docs/api/token.md

title	teaser	tag	source
Token	An individual token — i.e. a word, punctuation symbol, whitespace, etc.	class	spacy/tokens/token.pyx

Token.__init__

Construct a Token object.

Example

doc = nlp("Give it back! He pleaded.")
token = doc[0]
assert token.text == "Give"

Name	Type	Description
vocab	Vocab	A storage container for lexical types.
doc	Doc	The parent document.
offset	int	The index of the token within the document.
RETURNS	Token	The newly constructed object.

Token.__len__

The number of unicode characters in the token, i.e. token.text.

Example

doc = nlp("Give it back! He pleaded.")
token = doc[0]
assert len(token) == 4

Name	Type	Description
RETURNS	int	The number of unicode characters in the token.

Token.set_extension

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

Example

from spacy.tokens import Token
fruit_getter = lambda token: token.text in ("apple", "pear", "banana")
Token.set_extension("is_fruit", getter=fruit_getter)
doc = nlp("I have an apple")
assert doc[3]._.is_fruit

Name	Type	Description
name	unicode	Name of the attribute to set by the extension. For example, 'my_attr' will be available as token._.my_attr.
default	-	Optional default value of the attribute if no getter or method is defined.
method	callable	Set a custom method on the object, for example token._.compare(other_token).
getter	callable	Getter function that takes the object and returns an attribute value. Is called when the user accesses the ._ attribute.
setter	callable	Setter function that takes the Token and a value, and modifies the object. Is called when the user writes to the Token._ attribute.
force	bool	Force overwriting existing attribute.

Token.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 Token
Token.set_extension("is_fruit", default=False)
extension = Token.get_extension("is_fruit")
assert extension == (False, None, None, None)

Name	Type	Description
name	unicode	Name of the extension.
RETURNS	tuple	A (default, method, getter, setter) tuple of the extension.

Token.has_extension

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

Example

from spacy.tokens import Token
Token.set_extension("is_fruit", default=False)
assert Token.has_extension("is_fruit")

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

Token.remove_extension {#remove_extension tag="classmethod" new=""2.0.11""}

Remove a previously registered extension.

Example

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

Name	Type	Description
name	unicode	Name of the extension.
RETURNS	tuple	A (default, method, getter, setter) tuple of the removed extension.

Token.check_flag

Check the value of a boolean flag.

Example

from spacy.attrs import IS_TITLE
doc = nlp("Give it back! He pleaded.")
token = doc[0]
assert token.check_flag(IS_TITLE) == True

Name	Type	Description
flag_id	int	The attribute ID of the flag to check.
RETURNS	bool	Whether the flag is set.

Token.similarity

Compute a semantic similarity estimate. Defaults to cosine over vectors.

Example

apples, _, oranges = nlp("apples and oranges")
apples_oranges = apples.similarity(oranges)
oranges_apples = oranges.similarity(apples)
assert apples_oranges == oranges_apples

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

Token.nbor

Get a neighboring token.

Example

doc = nlp("Give it back! He pleaded.")
give_nbor = doc[0].nbor()
assert give_nbor.text == "it"

Name	Type	Description
i	int	The relative position of the token to get. Defaults to 1.
RETURNS	Token	The token at position self.doc[self.i+i].

Token.is_ancestor

Check whether this token is a parent, grandparent, etc. of another in the dependency tree.

Example

doc = nlp("Give it back! He pleaded.")
give = doc[0]
it = doc[1]
assert give.is_ancestor(it)

Name	Type	Description
descendant	Token	Another token.
RETURNS	bool	Whether this token is the ancestor of the descendant.

Token.ancestors

The rightmost token of this token's syntactic descendants.

Example

doc = nlp("Give it back! He pleaded.")
it_ancestors = doc[1].ancestors
assert [t.text for t in it_ancestors] == ["Give"]
he_ancestors = doc[4].ancestors
assert [t.text for t in he_ancestors] == ["pleaded"]

Name	Type	Description
YIELDS	Token	A sequence of ancestor tokens such that ancestor.is_ancestor(self).

Token.conjuncts

A tuple of coordinated tokens, not including the token itself.

Example

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

Name	Type	Description
RETURNS	tuple	The coordinated tokens.

Token.children

A sequence of the token's immediate syntactic children.

Example

doc = nlp("Give it back! He pleaded.")
give_children = doc[0].children
assert [t.text for t in give_children] == ["it", "back", "!"]

Name	Type	Description
YIELDS	Token	A child token such that child.head==self.

Token.lefts

The leftward immediate children of the word, in the syntactic dependency parse.

Example

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

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

Token.rights

The rightward immediate children of the word, in the syntactic dependency parse.

Example

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

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

Token.n_lefts

The number of leftward immediate children of the word, in the syntactic dependency parse.

Example

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

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

Token.n_rights

The number of rightward immediate children of the word, in the syntactic dependency parse.

Example

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

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

Token.subtree

A sequence containing the token and all the token's syntactic descendants.

Example

doc = nlp("Give it back! He pleaded.")
give_subtree = doc[0].subtree
assert [t.text for t in give_subtree] == ["Give", "it", "back", "!"]

Name	Type	Description
YIELDS	Token	A descendant token such that self.is_ancestor(token) or token == self.

Token.is_sent_start

A boolean value indicating whether the token starts a sentence. None if unknown. Defaults to True for the first token in the Doc.

Example

doc = nlp("Give it back! He pleaded.")
assert doc[4].is_sent_start
assert not doc[5].is_sent_start

Name	Type	Description
RETURNS	bool	Whether the token starts a sentence.

As of spaCy v2.0, the Token.sent_start property is deprecated and has been replaced with Token.is_sent_start, which returns a boolean value instead of a misleading 0 for False and 1 for True. It also now returns None if the answer is unknown, and fixes a quirk in the old logic that would always set the property to 0 for the first word of the document.

- assert doc[4].sent_start == 1
+ assert doc[4].is_sent_start == True

Token.has_vector

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

Example

doc = nlp("I like apples")
apples = doc[2]
assert apples.has_vector

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

Token.vector

A real-valued meaning representation.

Example

doc = nlp("I like apples")
apples = doc[2]
assert apples.vector.dtype == "float32"
assert apples.vector.shape == (300,)

Name	Type	Description
RETURNS	numpy.ndarray[ndim=1, dtype='float32']	A 1D numpy array representing the token's semantics.

Token.vector_norm

The L2 norm of the token's vector representation.

Example

doc = nlp("I like apples and pasta")
apples = doc[2]
pasta = doc[4]
apples.vector_norm  # 6.89589786529541
pasta.vector_norm  # 7.759851932525635
assert apples.vector_norm != pasta.vector_norm

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

Attributes

Name	Type	Description
doc	Doc	The parent document.
sent 2.0.12	Span	The sentence span that this token is a part of.
text	unicode	Verbatim text content.
text_with_ws	unicode	Text content, with trailing space character if present.
whitespace_	unicode	Trailing space character if present.
orth	int	ID of the verbatim text content.
orth_	unicode	Verbatim text content (identical to Token.text). Exists mostly for consistency with the other attributes.
vocab	Vocab	The vocab object of the parent Doc.
tensor 2.1.7	ndarray	The tokens's slice of the parent Doc's tensor.
head	Token	The syntactic parent, or "governor", of this token.
left_edge	Token	The leftmost token of this token's syntactic descendants.
right_edge	Token	The rightmost token of this token's syntactic descendants.
i	int	The index of the token within the parent document.
ent_type	int	Named entity type.
ent_type_	unicode	Named entity type.
ent_iob	int	IOB code of named entity tag. 3 means the token begins an entity, 2 means it is outside an entity, 1 means it is inside an entity, and 0 means no entity tag is set.
ent_iob_	unicode	IOB code of named entity tag. "B" means the token begins an entity, "I" means it is inside an entity, "O" means it is outside an entity, and "" means no entity tag is set.
ent_kb_id 2.2	int	Knowledge base ID that refers to the named entity this token is a part of, if any.
ent_kb_id_ 2.2	unicode	Knowledge base ID that refers to the named entity this token is a part of, if any.
ent_id	int	ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution.
ent_id_	unicode	ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution.
lemma	int	Base form of the token, with no inflectional suffixes.
lemma_	unicode	Base form of the token, with no inflectional suffixes.
norm	int	The token's norm, i.e. a normalized form of the token text. Usually set in the language's tokenizer exceptions or norm exceptions.
norm_	unicode	The token's norm, i.e. a normalized form of the token text. Usually set in the language's tokenizer exceptions or norm exceptions.
lower	int	Lowercase form of the token.
lower_	unicode	Lowercase form of the token text. Equivalent to Token.text.lower().
shape	int	Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by x or X, and numeric characters are replaced by d, and sequences of the same character are truncated after length 4. For example,"Xxxx"or"dd".
shape_	unicode	Transform of the tokens's string, to show orthographic features. Alphabetic characters are replaced by x or X, and numeric characters are replaced by d, and sequences of the same character are truncated after length 4. For example,"Xxxx"or"dd".
prefix	int	Hash value of a length-N substring from the start of the token. Defaults to N=1.
prefix_	unicode	A length-N substring from the start of the token. Defaults to N=1.
suffix	int	Hash value of a length-N substring from the end of the token. Defaults to N=3.
suffix_	unicode	Length-N substring from the end of the token. Defaults to N=3.
is_alpha	bool	Does the token consist of alphabetic characters? Equivalent to token.text.isalpha().
is_ascii	bool	Does the token consist of ASCII characters? Equivalent to all(ord(c) < 128 for c in token.text).
is_digit	bool	Does the token consist of digits? Equivalent to token.text.isdigit().
is_lower	bool	Is the token in lowercase? Equivalent to token.text.islower().
is_upper	bool	Is the token in uppercase? Equivalent to token.text.isupper().
is_title	bool	Is the token in titlecase? Equivalent to token.text.istitle().
is_punct	bool	Is the token punctuation?
is_left_punct	bool	Is the token a left punctuation mark, e.g. '(' ?
is_right_punct	bool	Is the token a right punctuation mark, e.g. ')' ?
is_space	bool	Does the token consist of whitespace characters? Equivalent to token.text.isspace().
is_bracket	bool	Is the token a bracket?
is_quote	bool	Is the token a quotation mark?
is_currency 2.0.8	bool	Is the token a currency symbol?
like_url	bool	Does the token resemble a URL?
like_num	bool	Does the token represent a number? e.g. "10.9", "10", "ten", etc.
like_email	bool	Does the token resemble an email address?
is_oov	bool	Is the token out-of-vocabulary?
is_stop	bool	Is the token part of a "stop list"?
pos	int	Coarse-grained part-of-speech from the Universal POS tag set.
pos_	unicode	Coarse-grained part-of-speech from the Universal POS tag set.
tag	int	Fine-grained part-of-speech.
tag_	unicode	Fine-grained part-of-speech.
dep	int	Syntactic dependency relation.
dep_	unicode	Syntactic dependency relation.
lang	int	Language of the parent document's vocabulary.
lang_	unicode	Language of the parent document's vocabulary.
prob	float	Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary).
idx	int	The character offset of the token within the parent document.
sentiment	float	A scalar value indicating the positivity or negativity of the token.
lex_id	int	Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors.
rank	int	Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors.
cluster	int	Brown cluster ID.
_	Underscore	User space for adding custom attribute extensions.