22 KiB
| 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. |
start |
The index of the first token of the span. |
end |
The index of the first token after the span. |
label |
A label to attach to the span, e.g. for named entities. |
kb_id |
A knowledge base ID to attach to the span, e.g. for named entities. |
vector |
A meaning representation of the span. |
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. |
| RETURNS | The token at span[i]. |
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. |
| RETURNS | The span at span[start : end]. |
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. |
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. |
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. |
default |
Optional default value of the attribute if no getter or method is defined. |
method |
Set a custom method on the object, for example span._.compare(other_span). |
getter |
Getter function that takes the object and returns an attribute value. Is called when the user accesses the ._ attribute. |
setter |
Setter function that takes the Span and a value, and modifies the object. Is called when the user writes to the Span._ attribute. |
force |
Force overwriting existing attribute. |
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. |
| RETURNS | A (default, method, getter, setter) tuple of the extension. |
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. |
| RETURNS | Whether the extension has been registered. |
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. |
| RETURNS | A (default, method, getter, setter) tuple of the removed extension. |
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. |
end |
The index of the last character after the span. |
label |
A label to attach to the span, e.g. for named entities. |
kb_id 2.2 |
An ID from a knowledge base to capture the meaning of a named entity. |
vector |
A meaning representation of the span. |
| RETURNS | The newly constructed object or None. |
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. |
| RETURNS | A scalar similarity score. Higher is more similar. |
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. |
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). |
| RETURNS | The exported attributes as a numpy array. |
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. |
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.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. |
| RETURNS | A Doc object of the Span's content. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
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. |
Attributes
| Name | Description |
|---|---|
doc |
The parent document. |
tensor 2.1.7 |
The span's slice of the parent Doc's tensor. |
sent |
The sentence span that this span is a part of. |
start |
The token offset for the start of the span. |
end |
The token offset for the end of the span. |
start_char |
The character offset for the start of the span. |
end_char |
The character offset for the end of the span. |
text |
A string representation of the span text. |
text_with_ws |
The text content of the span with a trailing whitespace character if the last token has one. |
orth |
ID of the verbatim text content. |
orth_ |
Verbatim text content (identical to Span.text). Exists mostly for consistency with the other attributes. |
label |
The hash value of the span's label. |
label_ |
The span's label. |
lemma_ |
The span's lemma. Equivalent to "".join(token.text_with_ws for token in span). |
kb_id |
The hash value of the knowledge base ID referred to by the span. |
kb_id_ |
The knowledge base ID referred to by the span. |
ent_id |
The hash value of the named entity the token is an instance of. |
ent_id_ |
The string ID of the named entity the token is an instance of. |
sentiment |
A scalar value indicating the positivity or negativity of the span. |
_ |
User space for adding custom attribute extensions. |