35 KiB
| title | tag | teaser | source |
|---|---|---|---|
| Doc | class | A container for accessing linguistic annotations. | spacy/tokens/doc.pyx |
A Doc is a sequence of Token objects. Access sentences and
named entities, export annotations to numpy arrays, losslessly serialize to
compressed binary strings. The Doc object holds an array of
TokenC structs. The Python-level Token and
Span objects are views of this array, i.e. they don't own the
data themselves.
Doc.__init__
Construct a Doc object. The most common way to get a Doc object is via the
nlp object.
Example
# Construction 1 doc = nlp("Some text") # Construction 2 from spacy.tokens import Doc words = ["hello", "world", "!"] spaces = [True, False, False] doc = Doc(nlp.vocab, words=words, spaces=spaces)
| Name | Description |
|---|---|
vocab |
A storage container for lexical types. |
words |
A list of strings to add to the container. |
spaces |
A list of boolean values indicating whether each word has a subsequent space. Must have the same length as words, if specified. Defaults to a sequence of True. |
Doc.__getitem__
Get a Token object at position i, where i is an integer.
Negative indexing is supported, and follows the usual Python semantics, i.e.
doc[-2] is doc[len(doc) - 2].
Example
doc = nlp("Give it back! He pleaded.") assert doc[0].text == "Give" assert doc[-1].text == "." span = doc[1:3] assert span.text == "it back"
| Name | Description |
|---|---|
i |
The index of the token. |
| RETURNS | The token at doc[i]. |
Get a Span object, starting at position start (token index) and
ending at position end (token index). For instance, doc[2:5] produces a span
consisting of tokens 2, 3 and 4. Stepped slices (e.g. doc[start : end : step])
are not supported, as Span objects must be contiguous (cannot have gaps). You
can use negative indices and open-ended ranges, which have their normal Python
semantics.
| Name | Description |
|---|---|
start_end |
The slice of the document to get. |
| RETURNS | The span at doc[start:end]. |
Doc.__iter__
Iterate over Token objects, from which the annotations can be easily accessed.
Example
doc = nlp("Give it back") assert [t.text for t in doc] == ["Give", "it", "back"]
This is the main way of accessing Token objects, which are the
main way annotations are accessed from Python. If faster-than-Python speeds are
required, you can instead access the annotations as a numpy array, or access the
underlying C data directly from Cython.
| Name | Description |
|---|---|
| YIELDS | A Token object. |
Doc.__len__
Get the number of tokens in the document.
Example
doc = nlp("Give it back! He pleaded.") assert len(doc) == 7
| Name | Description |
|---|---|
| RETURNS | The number of tokens in the document. |
Doc.set_extension
Define a custom attribute on the Doc which becomes available via Doc._. For
details, see the documentation on
custom attributes.
Example
from spacy.tokens import Doc city_getter = lambda doc: any(city in doc.text for city in ("New York", "Paris", "Berlin")) Doc.set_extension("has_city", getter=city_getter) doc = nlp("I like New York") assert doc._.has_city
| Name | Description |
|---|---|
name |
Name of the attribute to set by the extension. For example, "my_attr" will be available as doc._.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 doc._.compare(other_doc). |
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 Doc and a value, and modifies the object. Is called when the user writes to the Doc._ attribute. |
force |
Force overwriting existing attribute. |
Doc.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 Doc Doc.set_extension("has_city", default=False) extension = Doc.get_extension("has_city") assert extension == (False, None, None, None)
| Name | Description |
|---|---|
name |
Name of the extension. |
| RETURNS | A (default, method, getter, setter) tuple of the extension. |
Doc.has_extension
Check whether an extension has been registered on the Doc class.
Example
from spacy.tokens import Doc Doc.set_extension("has_city", default=False) assert Doc.has_extension("has_city")
| Name | Description |
|---|---|
name |
Name of the extension to check. |
| RETURNS | Whether the extension has been registered. |
Doc.remove_extension
Remove a previously registered extension.
Example
from spacy.tokens import Doc Doc.set_extension("has_city", default=False) removed = Doc.remove_extension("has_city") assert not Doc.has_extension("has_city")
| Name | Description |
|---|---|
name |
Name of the extension. |
| RETURNS | A (default, method, getter, setter) tuple of the removed extension. |
Doc.char_span
Create a Span object from the slice doc.text[start_idx:end_idx]. Returns
None if the character indices don't map to a valid span using the default mode
`"strict".
Example
doc = nlp("I like New York") span = doc.char_span(7, 15, 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. ~int~~ |
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. |
mode |
How character indices snap to token boundaries. Options: "strict" (no snapping), "inside" (span of all tokens completely within the character span), "outside" (span of all tokens at least partially covered by the character span). Defaults to "strict". |
| RETURNS | The newly constructed object or None. |
Doc.similarity
Make a semantic similarity estimate. The default estimate is cosine similarity using an average of word vectors.
Example
apples = nlp("I like apples") oranges = nlp("I like 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. |
| RETURNS | A scalar similarity score. Higher is more similar. |
Doc.count_by
Count the frequencies of a given attribute. Produces a dict of
{attr (int): count (ints)} frequencies, keyed by the values of the given
attribute ID.
Example
from spacy.attrs import ORTH doc = nlp("apple apple orange banana") assert doc.count_by(ORTH) == {7024: 1, 119552: 1, 2087: 2} doc.to_array([ORTH]) # array([[11880], [11880], [7561], [12800]])
| Name | Description |
|---|---|
attr_id |
The attribute ID. |
| RETURNS | A dictionary mapping attributes to integer counts. |
Doc.get_lca_matrix
Calculates the lowest common ancestor matrix for a given Doc. 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("This is a test") matrix = doc.get_lca_matrix() # array([[0, 1, 1, 1], [1, 1, 1, 1], [1, 1, 2, 3], [1, 1, 3, 3]], dtype=int32)
| Name | Description |
|---|---|
| RETURNS | The lowest common ancestor matrix of the Doc. |
Doc.to_array
Export given token attributes to a numpy ndarray. If attr_ids is a sequence
of M attributes, the output array will be of shape (N, M), where N is the
length of the Doc (in tokens). If attr_ids is a single attribute, the output
shape will be (N,). You can specify attributes by integer ID (e.g.
spacy.attrs.LEMMA) or string name (e.g. "LEMMA" or "lemma"). The values will
be 64-bit integers.
Returns a 2D array with one row per token and one column per attribute (when
attr_ids is a list), or as a 1D numpy array, with one item per attribute (when
attr_ids is a single value).
Example
from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA doc = nlp(text) # All strings mapped to integers, for easy export to numpy np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA]) np_array = doc.to_array("POS")
| 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. |
Doc.from_array
Load attributes from a numpy array. Write to a Doc object, from an (M, N)
array of attributes.
Example
from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA from spacy.tokens import Doc doc = nlp("Hello world!") np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA]) doc2 = Doc(doc.vocab, words=[t.text for t in doc]) doc2.from_array([LOWER, POS, ENT_TYPE, IS_ALPHA], np_array) assert doc[0].pos_ == doc2[0].pos_
| Name | Description |
|---|---|
attrs |
A list of attribute ID ints. |
array |
The attribute values to load. |
exclude |
String names of serialization fields to exclude. |
| RETURNS | The Doc itself. |
Doc.from_docs
Concatenate multiple Doc objects to form a new one. Raises an error if the
Doc objects do not all share the same Vocab.
Example
from spacy.tokens import Doc texts = ["London is the capital of the United Kingdom.", "The River Thames flows through London.", "The famous Tower Bridge crosses the River Thames."] docs = list(nlp.pipe(texts)) c_doc = Doc.from_docs(docs) assert str(c_doc) == " ".join(texts) assert len(list(c_doc.sents)) == len(docs) assert [str(ent) for ent in c_doc.ents] == \ [str(ent) for doc in docs for ent in doc.ents]
| Name | Description |
|---|---|
docs |
A list of Doc objects. |
ensure_whitespace |
Insert a space between two adjacent docs whenever the first doc does not end in whitespace. |
attrs |
Optional list of attribute ID ints or attribute name strings. |
| RETURNS | The new Doc object that is containing the other docs or None, if docs is empty or None. |
Doc.to_disk
Save the current state to a directory.
Example
doc.to_disk("/path/to/doc")
| Name | Description |
|---|---|
path |
A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects. |
| keyword-only | |
exclude |
String names of serialization fields to exclude. |
Doc.from_disk
Loads state from a directory. Modifies the object in place and returns it.
Example
from spacy.tokens import Doc from spacy.vocab import Vocab doc = Doc(Vocab()).from_disk("/path/to/doc")
| Name | Description |
|---|---|
path |
A path to a directory. Paths may be either strings or Path-like objects. |
| keyword-only | |
exclude |
String names of serialization fields to exclude. |
| RETURNS | The modified Doc object. |
Doc.to_bytes
Serialize, i.e. export the document contents to a binary string.
Example
doc = nlp("Give it back! He pleaded.") doc_bytes = doc.to_bytes()
| Name | Description |
|---|---|
| keyword-only | |
exclude |
String names of serialization fields to exclude. |
| RETURNS | A losslessly serialized copy of the Doc, including all annotations. |
Doc.from_bytes
Deserialize, i.e. import the document contents from a binary string.
Example
from spacy.tokens import Doc doc = nlp("Give it back! He pleaded.") doc_bytes = doc.to_bytes() doc2 = Doc(doc.vocab).from_bytes(doc_bytes) assert doc.text == doc2.text
| Name | Description |
|---|---|
data |
The string to load from. |
| keyword-only | |
exclude |
String names of serialization fields to exclude. |
| RETURNS | The Doc object. |
Doc.retokenize
Context manager to handle retokenization of the Doc. Modifications to the
Doc's tokenization are stored, and then made all at once when the context
manager exits. This is much more efficient, and less error-prone. All views of
the Doc (Span and Token) created before the retokenization are
invalidated, although they may accidentally continue to work.
Example
doc = nlp("Hello world!") with doc.retokenize() as retokenizer: retokenizer.merge(doc[0:2])
| Name | Description |
|---|---|
| RETURNS | The retokenizer. |
Retokenizer.merge
Mark a span for merging. The attrs will be applied to the resulting token (if
they're context-dependent token attributes like LEMMA or DEP) or to the
underlying lexeme (if they're context-independent lexical attributes like
LOWER or IS_STOP). Writable custom extension attributes can be provided as a
dictionary mapping attribute name to values as the "_" key.
Example
doc = nlp("I like David Bowie") with doc.retokenize() as retokenizer: attrs = {"LEMMA": "David Bowie"} retokenizer.merge(doc[2:4], attrs=attrs)
| Name | Description |
|---|---|
span |
The span to merge. |
attrs |
Attributes to set on the merged token. |
Retokenizer.split
Mark a token for splitting, into the specified orths. The heads are required
to specify how the new subtokens should be integrated into the dependency tree.
The list of per-token heads can either be a token in the original document, e.g.
doc[2], or a tuple consisting of the token in the original document and its
subtoken index. For example, (doc[3], 1) will attach the subtoken to the
second subtoken of doc[3].
This mechanism allows attaching subtokens to other newly created subtokens,
without having to keep track of the changing token indices. If the specified
head token will be split within the retokenizer block and no subtoken index is
specified, it will default to 0. Attributes to set on subtokens can be
provided as a list of values. They'll be applied to the resulting token (if
they're context-dependent token attributes like LEMMA or DEP) or to the
underlying lexeme (if they're context-independent lexical attributes like
LOWER or IS_STOP).
Example
doc = nlp("I live in NewYork") with doc.retokenize() as retokenizer: heads = [(doc[3], 1), doc[2]] attrs = {"POS": ["PROPN", "PROPN"], "DEP": ["pobj", "compound"]} retokenizer.split(doc[3], ["New", "York"], heads=heads, attrs=attrs)
| Name | Description |
|---|---|
token |
The token to split. |
orths |
The verbatim text of the split tokens. Needs to match the text of the original token. |
heads |
List of token or (token, subtoken) tuples specifying the tokens to attach the newly split subtokens to. |
attrs |
Attributes to set on all split tokens. Attribute names mapped to list of per-token attribute values. |
Doc.ents
The named entities in the document. 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.") ents = list(doc.ents) assert ents[0].label == 346 assert ents[0].label_ == "PERSON" assert ents[0].text == "Mr. Best"
| Name | Description |
|---|---|
| RETURNS | Entities in the document, one Span per entity. |
Doc.noun_chunks
Iterate over the base noun phrases in the document. 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.
Example
doc = nlp("A phrase with another phrase occurs.") chunks = list(doc.noun_chunks) assert chunks[0].text == "A phrase" assert chunks[1].text == "another phrase"
| Name | Description |
|---|---|
| YIELDS | Noun chunks in the document. |
Doc.sents
Iterate over the sentences in the document. Sentence spans have no label. To
improve accuracy on informal texts, spaCy calculates sentence boundaries from
the syntactic dependency parse. If the parser is disabled, the sents iterator
will be unavailable.
Example
doc = nlp("This is a sentence. Here's another...") sents = list(doc.sents) assert len(sents) == 2 assert [s.root.text for s in sents] == ["is", "'s"]
| Name | Description |
|---|---|
| YIELDS | Sentences in the document. |
Doc.has_vector
A boolean value indicating whether a word vector is associated with the object.
Example
doc = nlp("I like apples") assert doc.has_vector
| Name | Description |
|---|---|
| RETURNS | Whether the document has a vector data attached. |
Doc.vector
A real-valued meaning representation. Defaults to an average of the token vectors.
Example
doc = nlp("I like apples") assert doc.vector.dtype == "float32" assert doc.vector.shape == (300,)
| Name | Description |
|---|---|
| RETURNS | A 1-dimensional array representing the document's vector. |
Doc.vector_norm
The L2 norm of the document's vector representation.
Example
doc1 = nlp("I like apples") doc2 = nlp("I like oranges") doc1.vector_norm # 4.54232424414368 doc2.vector_norm # 3.304373298575751 assert doc1.vector_norm != doc2.vector_norm
| Name | Description |
|---|---|
| RETURNS | The L2 norm of the vector representation. |
Attributes
| Name | Description |
|---|---|
text |
A string representation of the document text. |
text_with_ws |
An alias of Doc.text, provided for duck-type compatibility with Span and Token. |
mem |
The document's local memory heap, for all C data it owns. |
vocab |
The store of lexical types. |
tensor 2 |
Container for dense vector representations. |
cats 2 |
Maps a label to a score for categories applied to the document. The label is a string and the score should be a float. |
user_data |
A generic storage area, for user custom data. |
lang 2.1 |
Language of the document's vocabulary. |
lang_ 2.1 |
Language of the document's vocabulary. |
is_tagged |
A flag indicating that the document has been part-of-speech tagged. Returns True if the Doc is empty. |
is_parsed |
A flag indicating that the document has been syntactically parsed. Returns True if the Doc is empty. |
is_sentenced |
A flag indicating that sentence boundaries have been applied to the document. Returns True if the Doc is empty. |
is_nered 2.1 |
A flag indicating that named entities have been set. Will return True if the Doc is empty, or if any of the tokens has an entity tag set, even if the others are unknown. |
sentiment |
The document's positivity/negativity score, if available. |
user_hooks |
A dictionary that allows customization of the Doc's properties. |
user_token_hooks |
A dictionary that allows customization of properties of Token children. |
user_span_hooks |
A dictionary that allows customization of properties of Span children. |
_ |
User space for adding custom attribute extensions. |
Serialization fields
During serialization, spaCy will export several data fields used to restore
different aspects of the object. If needed, you can exclude them from
serialization by passing in the string names via the exclude argument.
Example
data = doc.to_bytes(exclude=["text", "tensor"]) doc.from_disk("./doc.bin", exclude=["user_data"])
| Name | Description |
|---|---|
text |
The value of the Doc.text attribute. |
sentiment |
The value of the Doc.sentiment attribute. |
tensor |
The value of the Doc.tensor attribute. |
user_data |
The value of the Doc.user_data dictionary. |
user_data_keys |
The keys of the Doc.user_data dictionary. |
user_data_values |
The values of the Doc.user_data dictionary. |