spaCy/website/docs/api/doc.md

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.

Example

# Construction 1
doc = nlp(u"Some text")

# Construction 2
from spacy.tokens import Doc
words = [u"hello", u"world", u"!"]
spaces = [True, False, False]
doc = Doc(nlp.vocab, words=words, spaces=spaces)

Doc.__init__

Construct a Doc object. The most common way to get a Doc object is via the nlp object.

Name	Type	Description
vocab	Vocab	A storage container for lexical types.
words	iterable	A list of strings to add to the container.
spaces	iterable	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.
RETURNS	Doc	The newly constructed object.

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(u"Give it back! He pleaded.")
assert doc[0].text == "Give"
assert doc[-1].text == "."
span = doc[1:3]
assert span.text == "it back"

Name	Type	Description
i	int	The index of the token.
RETURNS	Token	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	Type	Description
start_end	tuple	The slice of the document to get.
RETURNS	Span	The span at doc[start:end].

Doc.__iter__

Iterate over Token objects, from which the annotations can be easily accessed.

Example

doc = nlp(u'Give it back')
assert [t.text for t in doc] == [u'Give', u'it', u'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	Type	Description
YIELDS	Token	A Token object.

Doc.__len__

Get the number of tokens in the document.

Example

doc = nlp(u"Give it back! He pleaded.")
assert len(doc) == 7

Name	Type	Description
RETURNS	int	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(u'I like New York')
assert doc._.has_city

Name	Type	Description
name	unicode	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	callable	Set a custom method on the object, for example doc._.compare(other_doc).
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 Doc and a value, and modifies the object. Is called when the user writes to the Doc._ 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	Type	Description
name	unicode	Name of the extension.
RETURNS	tuple	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	Type	Description
name	unicode	Name of the extension to check.
RETURNS	bool	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	Type	Description
name	unicode	Name of the extension.
RETURNS	tuple	A (default, method, getter, setter) tuple of the removed extension.

Doc.char_span

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

Example

doc = nlp(u"I like New York")
span = doc.char_span(7, 15, label=u"GPE")
assert span.text == "New York"

Name	Type	Description
start	int	The index of the first character of the span.
end	int	The index of the last character after the span.
label	uint64 / unicode	A label to attach to the Span, e.g. for named entities.
vector	numpy.ndarray[ndim=1, dtype='float32']	A meaning representation of the span.
RETURNS	Span	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(u"I like apples")
oranges = nlp(u"I like 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.

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(u"apple apple orange banana")
assert doc.count_by(ORTH) == {7024L: 1, 119552L: 1, 2087L: 2}
doc.to_array([attrs.ORTH])
# array([[11880], [11880], [7561], [12800]])

Name	Type	Description
attr_id	int	The attribute ID
RETURNS	dict	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(u"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	Type	Description
RETURNS	numpy.ndarray[ndim=2, dtype='int32']	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	Type	Description
attr_ids	list or int or string	A list of attributes (int IDs or string names) or a single attribute (int ID or string name)
RETURNS	numpy.ndarray[ndim=2, dtype='uint64'] or numpy.ndarray[ndim=1, dtype='uint64']	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(u"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	Type	Description
attrs	ints	A list of attribute ID ints.
array	numpy.ndarray[ndim=2, dtype='int32']	The attribute values to load.
RETURNS	Doc	Itself.

Doc.to_disk

Save the current state to a directory.

Example

doc.to_disk("/path/to/doc")

Name	Type	Description
path	unicode / Path	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or Path-like objects.

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	Type	Description
path	unicode / Path	A path to a directory. Paths may be either strings or Path-like objects.
RETURNS	Doc	The modified Doc object.

Doc.to_bytes

Serialize, i.e. export the document contents to a binary string.

Example

doc = nlp(u"Give it back! He pleaded.")
doc_bytes = doc.to_bytes()

Name	Type	Description
RETURNS	bytes	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
text = u"Give it back! He pleaded."
doc = nlp(text)
bytes = doc.to_bytes()
doc2 = Doc(doc.vocab).from_bytes(bytes)
assert doc.text == doc2.text

Name	Type	Description
data	bytes	The string to load from.
RETURNS	Doc	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	Type	Description
RETURNS	Retokenizer	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 names to values as the "_" key.

Example

doc = nlp(u"I like David Bowie")
with doc.retokenize() as retokenizer:
    attrs = {"LEMMA": u"David Bowie"}
    retokenizer.merge(doc[2:4], attrs=attrs)

Name	Type	Description
span	Span	The span to merge.
attrs	dict	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(u"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	Type	Description
token	Token	The token to split.
orths	list	The verbatim text of the split tokens. Needs to match the text of the original token.
heads	list	List of token or (token, subtoken) tuples specifying the tokens to attach the newly split subtokens to.
attrs	dict	Attributes to set on all split tokens. Attribute names mapped to list of per-token attribute values.

Doc.merge

As of v2.1.0, Doc.merge still works but is considered deprecated. You should use the new and less error-prone Doc.retokenize instead.

Retokenize the document, such that the span at doc.text[start_idx : end_idx] is merged into a single token. If start_idx and end_idx do not mark start and end token boundaries, the document remains unchanged.

Example

doc = nlp(u"Los Angeles start.")
doc.merge(0, len("Los Angeles"), "NNP", "Los Angeles", "GPE")
assert [t.text for t in doc] == [u"Los Angeles", u"start", u"."]

Name	Type	Description
start_idx	int	The character index of the start of the slice to merge.
end_idx	int	The character index after the end of the slice to merge.
**attributes	-	Attributes to assign to the merged token. By default, attributes are inherited from the syntactic root token of the span.
RETURNS	Token	The newly merged token, or None if the start and end indices did not fall at token boundaries

Doc.ents

Iterate over the entities in the document. Yields named-entity Span objects, if the entity recognizer has been applied to the document.

Example

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

Name	Type	Description
YIELDS	Span	Entities in the document.

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(u"A phrase with another phrase occurs.")
chunks = list(doc.noun_chunks)
assert chunks[0].text == u"A phrase"
assert chunks[1].text == u"another phrase"

Name	Type	Description
YIELDS	Span	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(u"This is a sentence. Here's another...")
sents = list(doc.sents)
assert len(sents) == 2
assert [s.root.text for s in sents] == [u"is", u"'s"]

Name	Type	Description
YIELDS	`Span	Sentences in the document.

Doc.has_vector

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

Example

doc = nlp(u"I like apples")
assert doc.has_vector

Name	Type	Description
RETURNS	bool	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(u"I like apples")
assert doc.vector.dtype == 'float32'
assert doc.vector.shape == (300,)

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

Doc.vector_norm

The L2 norm of the document's vector representation.

Example

doc1 = nlp(u"I like apples")
doc2 = nlp(u"I like oranges")
doc1.vector_norm  # 4.54232424414368
doc2.vector_norm  # 3.304373298575751
assert doc1.vector_norm != doc2.vector_norm

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

Attributes

Name	Type	Description
text	unicode	A unicode representation of the document text.
text_with_ws	unicode	An alias of Doc.text, provided for duck-type compatibility with Span and Token.
mem	Pool	The document's local memory heap, for all C data it owns.
vocab	Vocab	The store of lexical types.
tensor 2	object	Container for dense vector representations.
cats 2	dictionary	Maps either a label to a score for categories applied to whole document, or (start_char, end_char, label) to score for categories applied to spans. start_char and end_char should be character offsets, label can be either a string or an integer ID, and score should be a float.
user_data	-	A generic storage area, for user custom data.
is_tagged	bool	A flag indicating that the document has been part-of-speech tagged.
is_parsed	bool	A flag indicating that the document has been syntactically parsed.
is_sentenced	bool	A flag indicating that sentence boundaries have been applied to the document.
sentiment	float	The document's positivity/negativity score, if available.
user_hooks	dict	A dictionary that allows customization of the Doc's properties.
user_token_hooks	dict	A dictionary that allows customization of properties of Token children.
user_span_hooks	dict	A dictionary that allows customization of properties of Span children.
_	Underscore	User space for adding custom attribute extensions.