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//- 💫 DOCS > API > DOC
include ../../_includes/_mixins
p A container for accessing linguistic annotations.
p
| A #[code Doc] is a sequence of #[+api("token") #[code Token]] objects.
| Access sentences and named entities, export annotations to numpy arrays,
| losslessly serialize to compressed binary strings. The #[code Doc] object
| holds an array of #[code TokenC] structs. The Python-level #[code Token]
| and #[+api("span") #[code Span]] objects are views of this array, i.e.
| they don't own the data themselves.
+aside-code("Example").
# Construction 1
doc = nlp(u'Some text')
# Construction 2
from spacy.tokens import Doc
doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'],
spaces=[True, False, False])
+h(2, "init") Doc.__init__
+tag method
p
| Construct a #[code Doc] object. The most common way to get a #[code Doc]
| object is via the #[code nlp] object.
+table(["Name", "Type", "Description"])
+row
+cell #[code vocab]
+cell #[code Vocab]
+cell A storage container for lexical types.
+row
+cell #[code words]
+cell -
+cell A list of strings to add to the container.
+row
+cell #[code spaces]
+cell -
+cell
| A list of boolean values indicating whether each word has a
| subsequent space. Must have the same length as #[code words], if
| specified. Defaults to a sequence of #[code True].
+footrow
+cell returns
+cell #[code Doc]
+cell The newly constructed object.
+h(2, "getitem") Doc.__getitem__
+tag method
p
| Get a #[+api("token") #[code Token]] object at position #[code i], where
| #[code i] is an integer. Negative indexing is supported, and follows the
| usual Python semantics, i.e. #[code doc[-2]] is #[code doc[len(doc) - 2]].
+aside-code("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'
+table(["Name", "Type", "Description"])
+row
+cell #[code i]
+cell int
+cell The index of the token.
+footrow
+cell returns
+cell #[code Token]
+cell The token at #[code doc[i]].
p
| Get a #[+api("span") #[code Span]] object, starting at position
| #[code start] (token index) and ending at position #[code end] (token
| index).
p
| For instance, #[code doc[2:5]] produces a span consisting of tokens 2, 3
| and 4. Stepped slices (e.g. #[code doc[start : end : step]]) are not
| supported, as #[code Span] objects must be contiguous (cannot have gaps).
| You can use negative indices and open-ended ranges, which have their
| normal Python semantics.
+table(["Name", "Type", "Description"])
+row
+cell #[code start_end]
+cell tuple
+cell The slice of the document to get.
+footrow
+cell returns
+cell #[code Span]
+cell The span at #[code doc[start : end]].
+h(2, "iter") Doc.__iter__
+tag method
p
| Iterate over #[code Token] objects, from which the annotations can be
| easily accessed.
+aside-code("Example").
doc = nlp(u'Give it back')
assert [t.text for t in doc] == [u'Give', u'it', u'back']
p
| This is the main way of accessing #[+api("token") #[code 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.
+table(["Name", "Type", "Description"])
+footrow
+cell yields
+cell #[code Token]
+cell A #[code Token] object.
+h(2, "len") Doc.__len__
+tag method
p Get the number of tokens in the document.
+aside-code("Example").
doc = nlp(u'Give it back! He pleaded.')
assert len(doc) == 7
+table(["Name", "Type", "Description"])
+footrow
+cell returns
+cell int
+cell The number of tokens in the document.
+h(2, "char_span") Doc.char_span
+tag method
+tag-new(2)
p Create a #[code Span] object from the slice #[code doc.text[start : end]].
+aside-code("Example").
doc = nlp(u'I like New York')
span = doc.char_span(7, 15, label=u'GPE')
assert span.text == 'New York'
+table(["Name", "Type", "Description"])
+row
+cell #[code start]
+cell int
+cell The index of the first character of the span.
+row
+cell #[code end]
+cell int
+cell The index of the first character after the span.
+row
+cell #[code label]
+cell uint64 / unicode
+cell A label to attach to the Span, e.g. for named entities.
+row
+cell #[code vector]
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+cell A meaning representation of the span.
+footrow
+cell returns
+cell #[code Span]
+cell The newly constructed object.
+h(2, "similarity") Doc.similarity
+tag method
+tag-model("vectors")
p
| Make a semantic similarity estimate. The default estimate is cosine
| similarity using an average of word vectors.
+aside-code("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
+table(["Name", "Type", "Description"])
+row
+cell #[code other]
+cell -
+cell
| The object to compare with. By default, accepts #[code Doc],
| #[code Span], #[code Token] and #[code Lexeme] objects.
+footrow
+cell returns
+cell float
+cell A scalar similarity score. Higher is more similar.
+h(2, "count_by") Doc.count_by
+tag method
p
| Count the frequencies of a given attribute. Produces a dict of
| #[code {attr (int): count (ints)}] frequencies, keyed by the values
| of the given attribute ID.
+aside-code("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]])
+table(["Name", "Type", "Description"])
+row
+cell #[code attr_id]
+cell int
+cell The attribute ID
+footrow
+cell returns
+cell dict
+cell A dictionary mapping attributes to integer counts.
+h(2, "to_array") Doc.to_array
+tag method
p
| Export the document annotations to a numpy array of shape #[code N*M]
| where #[code N] is the length of the document and #[code M] is the number
| of attribute IDs to export. The values will be 32-bit integers.
+aside-code("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])
+table(["Name", "Type", "Description"])
+row
+cell #[code attr_ids]
+cell list
+cell A list of attribute ID ints.
+footrow
+cell returns
+cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
+cell
| The exported attributes as a 2D numpy array, with one row per
| token and one column per attribute.
+h(2, "from_array") Doc.from_array
+tag method
p
| Load attributes from a numpy array. Write to a #[code Doc] object, from
| an #[code (M, N)] array of attributes.
+aside-code("Example").
from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
from spacy.tokens import Doc
doc = nlp(text)
np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
doc2 = Doc(doc.vocab)
doc2.from_array([LOWER, POS, ENT_TYPE, IS_ALPHA], np_array)
assert doc.text == doc2.text
+table(["Name", "Type", "Description"])
+row
+cell #[code attrs]
+cell ints
+cell A list of attribute ID ints.
+row
+cell #[code array]
+cell #[code.u-break numpy.ndarray[ndim=2, dtype='int32']]
+cell The attribute values to load.
+footrow
+cell returns
+cell #[code Doc]
+cell Itself.
+h(2, "to_disk") Doc.to_disk
+tag method
+tag-new(2)
p Save the current state to a directory.
+aside-code("Example").
doc.to_disk('/path/to/doc')
+table(["Name", "Type", "Description"])
+row
+cell #[code path]
+cell unicode or #[code Path]
+cell
| A path to a directory, which will be created if it doesn't exist.
| Paths may be either strings or #[code Path]-like objects.
+h(2, "from_disk") Doc.from_disk
+tag method
+tag-new(2)
p Loads state from a directory. Modifies the object in place and returns it.
+aside-code("Example").
from spacy.tokens import Doc
from spacy.vocab import Vocab
doc = Doc(Vocab()).from_disk('/path/to/doc')
+table(["Name", "Type", "Description"])
+row
+cell #[code path]
+cell unicode or #[code Path]
+cell
| A path to a directory. Paths may be either strings or
| #[code Path]-like objects.
+footrow
+cell returns
+cell #[code Doc]
+cell The modified #[code Doc] object.
+h(2, "to_bytes") Doc.to_bytes
+tag method
p Serialize, i.e. export the document contents to a binary string.
+aside-code("Example").
doc = nlp(u'Give it back! He pleaded.')
doc_bytes = doc.to_bytes()
+table(["Name", "Type", "Description"])
+footrow
+cell returns
+cell bytes
+cell
| A losslessly serialized copy of the #[code Doc], including all
| annotations.
+h(2, "from_bytes") Doc.from_bytes
+tag method
p Deserialize, i.e. import the document contents from a binary string.
+aside-code("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
+table(["Name", "Type", "Description"])
+row
+cell #[code data]
+cell bytes
+cell The string to load from.
+footrow
+cell returns
+cell #[code Doc]
+cell The #[code Doc] object.
+h(2, "merge") Doc.merge
+tag method
p
| Retokenize the document, such that the span at
| #[code doc.text[start_idx : end_idx]] is merged into a single token. If
| #[code start_idx] and #[end_idx] do not mark start and end token
| boundaries, the document remains unchanged.
+aside-code("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'.']
+table(["Name", "Type", "Description"])
+row
+cell #[code start_idx]
+cell int
+cell The character index of the start of the slice to merge.
+row
+cell #[code end_idx]
+cell int
+cell The character index after the end of the slice to merge.
+row
+cell #[code **attributes]
+cell -
+cell
| Attributes to assign to the merged token. By default,
| attributes are inherited from the syntactic root token of
| the span.
+footrow
+cell returns
+cell #[code Token]
+cell
| The newly merged token, or #[code None] if the start and end
| indices did not fall at token boundaries
+h(2, "print_tree") Doc.print_tree
+tag method
+tag-model("parse")
p
| Returns the parse trees in JSON (dict) format. Especially useful for
| web applications.
+aside-code("Example").
doc = nlp('Alice ate the pizza.')
trees = doc.print_tree()
# {'modifiers': [
# {'modifiers': [], 'NE': 'PERSON', 'word': 'Alice', 'arc': 'nsubj', 'POS_coarse': 'PROPN', 'POS_fine': 'NNP', 'lemma': 'Alice'},
# {'modifiers': [{'modifiers': [], 'NE': '', 'word': 'the', 'arc': 'det', 'POS_coarse': 'DET', 'POS_fine': 'DT', 'lemma': 'the'}], 'NE': '', 'word': 'pizza', 'arc': 'dobj', 'POS_coarse': 'NOUN', 'POS_fine': 'NN', 'lemma': 'pizza'},
# {'modifiers': [], 'NE': '', 'word': '.', 'arc': 'punct', 'POS_coarse': 'PUNCT', 'POS_fine': '.', 'lemma': '.'}
# ], 'NE': '', 'word': 'ate', 'arc': 'ROOT', 'POS_coarse': 'VERB', 'POS_fine': 'VBD', 'lemma': 'eat'}
+table(["Name", "Type", "Description"])
+row
+cell #[code light]
+cell bool
+cell Don't include lemmas or entities.
+row
+cell #[code flat]
+cell bool
+cell Don't include arcs or modifiers.
+footrow
+cell returns
+cell dict
+cell Parse tree as dict.
+h(2, "ents") Doc.ents
+tag property
+tag-model("NER")
p
| Iterate over the entities in the document. Yields named-entity
| #[code Span] objects, if the entity recognizer has been applied to the
| document.
+aside-code("Example").
tokens = nlp(u'Mr. Best flew to New York on Saturday morning.')
ents = list(tokens.ents)
assert ents[0].label == 346
assert ents[0].label_ == 'PERSON'
assert ents[0].text == 'Mr. Best'
+table(["Name", "Type", "Description"])
+footrow
+cell yields
+cell #[code Span]
+cell Entities in the document.
+h(2, "noun_chunks") Doc.noun_chunks
+tag property
+tag-model("parse")
p
| Iterate over the base noun phrases in the document. Yields base
| noun-phrase #[code 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.
+aside-code("Example").
doc = nlp(u'A phrase with another phrase occurs.')
chunks = list(doc.noun_chunks)
assert chunks[0].text == "A phrase"
assert chunks[1].text == "another phrase"
+table(["Name", "Type", "Description"])
+footrow
+cell yields
+cell #[code Span]
+cell Noun chunks in the document.
+h(2, "sents") Doc.sents
+tag property
+tag-model("parse")
p
| 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 #[code sents] iterator will be unavailable.
+aside-code("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] == ["is", "'s"]
+table(["Name", "Type", "Description"])
+footrow
+cell yields
+cell #[code Span]
+cell Sentences in the document.
+h(2, "has_vector") Doc.has_vector
+tag property
+tag-model("vectors")
p
| A boolean value indicating whether a word vector is associated with the
| object.
+aside-code("Example").
doc = nlp(u'I like apples')
assert doc.has_vector
+table(["Name", "Type", "Description"])
+footrow
+cell returns
+cell bool
+cell Whether the document has a vector data attached.
+h(2, "vector") Doc.vector
+tag property
+tag-model("vectors")
p
| A real-valued meaning representation. Defaults to an average of the
| token vectors.
+aside-code("Example").
apples = nlp(u'I like apples')
assert doc.vector.dtype == 'float32'
assert doc.vector.shape == (300,)
+table(["Name", "Type", "Description"])
+footrow
+cell returns
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+cell A 1D numpy array representing the document's semantics.
+h(2, "vector_norm") Doc.vector_norm
+tag property
+tag-model("vectors")
p
| The L2 norm of the document's vector representation.
+aside-code("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
+table(["Name", "Type", "Description"])
+footrow
+cell returns
+cell float
+cell The L2 norm of the vector representation.
+h(2, "attributes") Attributes
+table(["Name", "Type", "Description"])
+row
+cell #[code text]
+cell unicode
+cell A unicode representation of the document text.
+row
+cell #[code text_with_ws]
+cell unicode
+cell
| An alias of #[code Doc.text], provided for duck-type compatibility
| with #[code Span] and #[code Token].
+row
+cell #[code mem]
+cell #[code Pool]
+cell The document's local memory heap, for all C data it owns.
+row
+cell #[code vocab]
+cell #[code Vocab]
+cell The store of lexical types.
+row
+cell #[code tensor] #[+tag-new(2)]
+cell object
+cell Container for dense vector representations.
+row
+cell #[code cats] #[+tag-new(2)]
+cell dictionary
+cell
| Maps either a label to a score for categories applied to whole
| document, or #[code (start_char, end_char, label)] to score for
| categories applied to spans. #[code start_char] and #[code end_char]
| should be character offsets, label can be either a string or an
| integer ID, and score should be a float.
+row
+cell #[code user_data]
+cell -
+cell A generic storage area, for user custom data.
+row
+cell #[code is_tagged]
+cell bool
+cell
| A flag indicating that the document has been part-of-speech
| tagged.
+row
+cell #[code is_parsed]
+cell bool
+cell A flag indicating that the document has been syntactically parsed.
+row
+cell #[code sentiment]
+cell float
+cell The document's positivity/negativity score, if available.
+row
+cell #[code user_hooks]
+cell dict
+cell
| A dictionary that allows customisation of the #[code Doc]'s
| properties.
+row
+cell #[code user_token_hooks]
+cell dict
+cell
| A dictionary that allows customisation of properties of
| #[code Token] children.
+row
+cell #[code user_span_hooks]
+cell dict
+cell
| A dictionary that allows customisation of properties of
| #[code Span] children.
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