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	Description
vocab	A storage container for lexical types. Vocab
doc	The parent document. Doc
offset	The index of the token within the document. int

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

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	Description
name	Name of the attribute to set by the extension. For example, "my_attr" will be available as token._.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 token._.compare(other_token). Optional[CallableToken, ...], Any
getter	Getter function that takes the object and returns an attribute value. Is called when the user accesses the ._ attribute. Optional[CallableToken], Any
setter	Setter function that takes the Token and a value, and modifies the object. Is called when the user writes to the Token._ attribute. Optional[CallableToken, Any], None
force	Force overwriting existing attribute. bool

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	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]]

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

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	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]]

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

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	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

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

Token.set_morph

Set the morphological analysis from a UD FEATS string, hash value of a UD FEATS string, features dict or MorphAnalysis. The value None can be used to reset the morph to an unset state.

Example

doc = nlp("Give it back! He pleaded.")
doc[0].set_morph("Mood=Imp|VerbForm=Fin")
assert "Mood=Imp" in doc[0].morph
assert doc[0].morph.get("Mood") == ["Imp"]

Name	Description
features	The morphological features to set. Union[int, dict, str, MorphAnalysis, None]

Token.has_morph

Check whether the token has annotated morph information. Return False when the morph annotation is unset/missing.

Name	Description
RETURNS	Whether the morph annotation is set. bool

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

Token.ancestors

A sequence of the token's syntactic ancestors (parents, grandparents, etc).

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

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

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

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	Description
YIELDS	A left-child of the token. 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	Description
YIELDS	A right-child of the token. 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	Description
RETURNS	The number of left-child tokens. int

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	Description
RETURNS	The number of right-child tokens. int

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

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

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

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	Description
RETURNS	The L2 norm of the vector representation. float

Attributes

Name	Description
doc	The parent document. Doc
lex 3	The underlying lexeme. Lexeme
sent 2.0.12	The sentence span that this token is a part of. Span
text	Verbatim text content. str
text_with_ws	Text content, with trailing space character if present. str
whitespace_	Trailing space character if present. str
orth	ID of the verbatim text content. int
orth_	Verbatim text content (identical to Token.text). Exists mostly for consistency with the other attributes. str
vocab	The vocab object of the parent Doc. vocab
tensor 2.1.7	The token's slice of the parent Doc's tensor. numpy.ndarray
head	The syntactic parent, or "governor", of this token. Token
left_edge	The leftmost token of this token's syntactic descendants. Token
right_edge	The rightmost token of this token's syntactic descendants. Token
i	The index of the token within the parent document. int
ent_type	Named entity type. int
ent_type_	Named entity type. str
ent_iob	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. int
ent_iob_	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. str
ent_kb_id 2.2	Knowledge base ID that refers to the named entity this token is a part of, if any. int
ent_kb_id_ 2.2	Knowledge base ID that refers to the named entity this token is a part of, if any. str
ent_id	ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. int
ent_id_	ID of the entity the token is an instance of, if any. Currently not used, but potentially for coreference resolution. str
lemma	Base form of the token, with no inflectional suffixes. int
lemma_	Base form of the token, with no inflectional suffixes. str
norm	The token's norm, i.e. a normalized form of the token text. Can be set in the language's tokenizer exceptions. int
norm_	The token's norm, i.e. a normalized form of the token text. Can be set in the language's tokenizer exceptions. str
lower	Lowercase form of the token. int
lower_	Lowercase form of the token text. Equivalent to Token.text.lower(). str
shape	Transform of the token'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". int
shape_	Transform of the token'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". str
prefix	Hash value of a length-N substring from the start of the token. Defaults to N=1. int
prefix_	A length-N substring from the start of the token. Defaults to N=1. str
suffix	Hash value of a length-N substring from the end of the token. Defaults to N=3. int
suffix_	Length-N substring from the end of the token. Defaults to N=3. str
is_alpha	Does the token consist of alphabetic characters? Equivalent to token.text.isalpha(). bool
is_ascii	Does the token consist of ASCII characters? Equivalent to all(ord(c) < 128 for c in token.text). bool
is_digit	Does the token consist of digits? Equivalent to token.text.isdigit(). bool
is_lower	Is the token in lowercase? Equivalent to token.text.islower(). bool
is_upper	Is the token in uppercase? Equivalent to token.text.isupper(). bool
is_title	Is the token in titlecase? Equivalent to token.text.istitle(). bool
is_punct	Is the token punctuation? bool
is_left_punct	Is the token a left punctuation mark, e.g. "(" ? bool
is_right_punct	Is the token a right punctuation mark, e.g. ")" ? bool
is_sent_start	Does the token start a sentence? bool or None if unknown. Defaults to True for the first token in the Doc.
is_sent_end	Does the token end a sentence? bool or None if unknown.
is_space	Does the token consist of whitespace characters? Equivalent to token.text.isspace(). bool
is_bracket	Is the token a bracket? bool
is_quote	Is the token a quotation mark? bool
is_currency 2.0.8	Is the token a currency symbol? bool
like_url	Does the token resemble a URL? bool
like_num	Does the token represent a number? e.g. "10.9", "10", "ten", etc. bool
like_email	Does the token resemble an email address? bool
is_oov	Is the token out-of-vocabulary (i.e. does it not have a word vector)? bool
is_stop	Is the token part of a "stop list"? bool
pos	Coarse-grained part-of-speech from the Universal POS tag set. int
pos_	Coarse-grained part-of-speech from the Universal POS tag set. str
tag	Fine-grained part-of-speech. int
tag_	Fine-grained part-of-speech. str
morph 3	Morphological analysis. MorphAnalysis
dep	Syntactic dependency relation. int
dep_	Syntactic dependency relation. str
lang	Language of the parent document's vocabulary. int
lang_	Language of the parent document's vocabulary. str
prob	Smoothed log probability estimate of token's word type (context-independent entry in the vocabulary). float
idx	The character offset of the token within the parent document. int
sentiment	A scalar value indicating the positivity or negativity of the token. float
lex_id	Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. int
rank	Sequential ID of the token's lexical type, used to index into tables, e.g. for word vectors. int
cluster	Brown cluster ID. int
_	User space for adding custom attribute extensions. Underscore