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566 lines
16 KiB
Plaintext
566 lines
16 KiB
Plaintext
//- 💫 DOCS > API > DOC
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include ../../_includes/_mixins
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p A container for accessing linguistic annotations.
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p
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| A #[code Doc] is a sequence of #[+api("token") #[code Token]] objects.
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| Access sentences and named entities, export annotations to numpy arrays,
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| losslessly serialize to compressed binary strings. The #[code Doc] object
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| holds an array of #[code TokenC] structs. The Python-level #[code Token]
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| and #[+api("span") #[code Span]] objects are views of this array, i.e.
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| they don't own the data themselves.
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+aside-code("Example").
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# Construction 1
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doc = nlp(u'Some text')
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# Construction 2
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from spacy.tokens import Doc
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doc = doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'],
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spaces=[True, False, False])
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+h(2, "init") Doc.__init__
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+tag method
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p
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| Construct a #[code Doc] object. The most common way to get a #[code Doc]
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| object is via the #[code nlp] object.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell A storage container for lexical types.
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+row
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+cell #[code words]
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+cell -
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+cell A list of strings to add to the container.
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+row
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+cell #[code spaces]
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+cell -
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+cell
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| A list of boolean values indicating whether each word has a
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| subsequent space. Must have the same length as #[code words], if
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| specified. Defaults to a sequence of #[code True].
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+footrow
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+cell returns
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+cell #[code Doc]
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+cell The newly constructed object.
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+h(2, "getitem") Doc.__getitem__
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+tag method
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p
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| Get a #[+api("token") #[code Token]] object at position #[code i], where
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| #[code i] is an integer. Negative indexing is supported, and follows the
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| usual Python semantics, i.e. #[code doc[-2]] is #[code doc[len(doc) - 2]].
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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assert doc[0].text == 'Give'
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assert doc[-1].text == '.'
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span = doc[1:1]
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assert span.text == 'it back'
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code i]
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+cell int
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+cell The index of the token.
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+footrow
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+cell returns
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+cell #[code Token]
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+cell The token at #[code doc[i]].
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p
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| Get a #[+api("span") #[code Span]] object, starting at position
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| #[code start] (token index) and ending at position #[code end] (token
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| index).
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p
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| For instance, #[code doc[2:5]] produces a span consisting of tokens 2, 3
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| and 4. Stepped slices (e.g. #[code doc[start : end : step]]) are not
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| supported, as #[code Span] objects must be contiguous (cannot have gaps).
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| You can use negative indices and open-ended ranges, which have their
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| normal Python semantics.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code start_end]
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+cell tuple
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+cell The slice of the document to get.
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+footrow
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+cell returns
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+cell #[code Span]
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+cell The span at #[code doc[start : end]].
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+h(2, "iter") Doc.__iter__
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+tag method
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p
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| Iterate over #[code Token] objects, from which the annotations can be
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| easily accessed.
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+aside-code("Example").
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doc = nlp(u'Give it back')
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assert [t.text for t in doc] == [u'Give', u'it', u'back']
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p
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| This is the main way of accessing #[+api("token") #[code Token]] objects,
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| which are the main way annotations are accessed from Python. If
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| faster-than-Python speeds are required, you can instead access the
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| annotations as a numpy array, or access the underlying C data directly
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| from Cython.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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+cell #[code Token]
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+cell A #[code Token] object.
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+h(2, "len") Doc.__len__
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+tag method
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p Get the number of tokens in the document.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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assert len(doc) == 7
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell int
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+cell The number of tokens in the document.
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+h(2, "similarity") Doc.similarity
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+tag method
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+tag-model("vectors")
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p
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| Make a semantic similarity estimate. The default estimate is cosine
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| similarity using an average of word vectors.
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+aside-code("Example").
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apples = nlp(u'I like apples')
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oranges = nlp(u'I like oranges')
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apples_oranges = apples.similarity(oranges)
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oranges_apples = oranges.similarity(apples)
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assert apples_oranges == oranges_apples
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code other]
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+cell -
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+cell
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| The object to compare with. By default, accepts #[code Doc],
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| #[code Span], #[code Token] and #[code Lexeme] objects.
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+footrow
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+cell returns
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+cell float
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+cell A scalar similarity score. Higher is more similar.
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+h(2, "count_by") Doc.count_by
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+tag method
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p
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| Count the frequencies of a given attribute. Produces a dict of
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| #[code {attr (int): count (ints)}] frequencies, keyed by the values
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| of the given attribute ID.
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+aside-code("Example").
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from spacy.attrs import ORTH
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doc = nlp(u'apple apple orange banana')
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assert doc.count_by(ORTH) == {7024L: 1, 119552L: 1, 2087L: 2}
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doc.to_array([attrs.ORTH])
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# array([[11880], [11880], [7561], [12800]])
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code attr_id]
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+cell int
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+cell The attribute ID
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+footrow
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+cell returns
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+cell dict
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+cell A dictionary mapping attributes to integer counts.
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+h(2, "to_array") Doc.to_array
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+tag method
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p
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| Export the document annotations to a numpy array of shape #[code N*M]
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| where #[code N] is the length of the document and #[code M] is the number
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| of attribute IDs to export. The values will be 32-bit integers.
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+aside-code("Example").
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from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
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doc = nlp(text)
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# All strings mapped to integers, for easy export to numpy
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np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code attr_ids]
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+cell ints
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+cell A list of attribute ID ints.
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+footrow
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+cell returns
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+cell #[code numpy.ndarray[ndim=2, dtype='int32']]
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+cell
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| The exported attributes as a 2D numpy array, with one row per
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| token and one column per attribute.
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+h(2, "from_array") Doc.from_array
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+tag method
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p
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| Load attributes from a numpy array. Write to a #[code Doc] object, from
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| an #[code (M, N)] array of attributes.
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+aside-code("Example").
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from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
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from spacy.tokens import Doc
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doc = nlp(text)
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np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
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doc2 = Doc(doc.vocab)
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doc2.from_array([LOWER, POS, ENT_TYPE, IS_ALPHA], np_array)
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assert doc.text == doc2.text
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code attrs]
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+cell ints
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+cell A list of attribute ID ints.
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+row
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+cell #[code array]
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+cell #[code numpy.ndarray[ndim=2, dtype='int32']]
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+cell The attribute values to load.
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+footrow
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+cell returns
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+cell #[code Doc]
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+cell Itself.
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+h(2, "to_bytes") Doc.to_bytes
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+tag method
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p Serialize, i.e. export the document contents to a binary string.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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doc_bytes = doc.to_bytes()
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell bytes
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+cell
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| A losslessly serialized copy of the #[code Doc], including all
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| annotations.
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+h(2, "from_bytes") Doc.from_bytes
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+tag method
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p Deserialize, i.e. import the document contents from a binary string.
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+aside-code("Example").
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from spacy.tokens import Doc
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text = u'Give it back! He pleaded.'
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doc = nlp(text)
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bytes = doc.to_bytes()
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doc2 = Doc(doc.vocab).from_bytes(bytes)
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assert doc.text == doc2.text
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code data]
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+cell bytes
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+cell The string to load from.
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+footrow
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+cell returns
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+cell #[code Doc]
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+cell Itself.
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+h(2, "merge") Doc.merge
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+tag method
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p
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| Retokenize the document, such that the span at
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| #[code doc.text[start_idx : end_idx]] is merged into a single token. If
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| #[code start_idx] and #[end_idx] do not mark start and end token
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| boundaries, the document remains unchanged.
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+aside-code("Example").
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doc = nlp(u'Los Angeles start.')
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doc.merge(0, len('Los Angeles'), 'NNP', 'Los Angeles', 'GPE')
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assert [t.text for t in doc] == [u'Los Angeles', u'start', u'.']
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code start_idx]
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+cell int
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+cell The character index of the start of the slice to merge.
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+row
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+cell #[code end_idx]
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+cell int
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+cell The character index after the end of the slice to merge.
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+row
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+cell #[code **attributes]
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+cell -
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+cell
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| Attributes to assign to the merged token. By default,
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| attributes are inherited from the syntactic root token of
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| the span.
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+footrow
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+cell returns
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+cell #[code Token]
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+cell
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| The newly merged token, or #[code None] if the start and end
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| indices did not fall at token boundaries
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+h(2, "print_tree") Doc.print_tree
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+tag method
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+tag-model("parse")
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p
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| Returns the parse trees in JSON (dict) format. Especially useful for
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| web applications.
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+aside-code("Example").
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doc = nlp('Alice ate the pizza.')
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trees = doc.print_tree()
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# {'modifiers': [
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# {'modifiers': [], 'NE': 'PERSON', 'word': 'Alice', 'arc': 'nsubj', 'POS_coarse': 'PROPN', 'POS_fine': 'NNP', 'lemma': 'Alice'},
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# {'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'},
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# {'modifiers': [], 'NE': '', 'word': '.', 'arc': 'punct', 'POS_coarse': 'PUNCT', 'POS_fine': '.', 'lemma': '.'}
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# ], 'NE': '', 'word': 'ate', 'arc': 'ROOT', 'POS_coarse': 'VERB', 'POS_fine': 'VBD', 'lemma': 'eat'}
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code light]
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+cell bool
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+cell Don't include lemmas or entities.
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+row
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+cell #[code flat]
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+cell bool
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+cell Don't include arcs or modifiers.
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+footrow
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+cell returns
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+cell dict
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+cell Parse tree as dict.
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+h(2, "ents") Doc.ents
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+tag property
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+tag-model("NER")
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p
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| Iterate over the entities in the document. Yields named-entity
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| #[code Span] objects, if the entity recognizer has been applied to the
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| document.
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+aside-code("Example").
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tokens = nlp(u'Mr. Best flew to New York on Saturday morning.')
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ents = list(tokens.ents)
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assert ents[0].label == 346
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assert ents[0].label_ == 'PERSON'
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assert ents[0].text == 'Mr. Best'
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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+cell #[code Span]
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+cell Entities in the document.
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+h(2, "noun_chunks") Doc.noun_chunks
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+tag property
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+tag-model("parse")
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p
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| Iterate over the base noun phrases in the document. Yields base
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| noun-phrase #[code Span] objects, if the document has been syntactically
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| parsed. A base noun phrase, or "NP chunk", is a noun phrase that does not
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| permit other NPs to be nested within it – so no NP-level coordination, no
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| prepositional phrases, and no relative clauses.
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+aside-code("Example").
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doc = nlp(u'A phrase with another phrase occurs.')
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chunks = list(doc.noun_chunks)
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assert chunks[0].text == "A phrase"
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assert chunks[1].text == "another phrase"
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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+cell #[code Span]
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+cell Noun chunks in the document.
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+h(2, "sents") Doc.sents
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+tag property
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+tag-model("parse")
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p
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| Iterate over the sentences in the document. Sentence spans have no label.
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| To improve accuracy on informal texts, spaCy calculates sentence boundaries
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| from the syntactic dependency parse. If the parser is disabled,
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| the #[code sents] iterator will be unavailable.
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+aside-code("Example").
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doc = nlp(u"This is a sentence. Here's another...")
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sents = list(doc.sents)
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assert len(sents) == 2
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assert [s.root.text for s in sents] == ["is", "'s"]
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yields
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+cell #[code Span]
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+cell Sentences in the document.
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+h(2, "has_vector") Doc.has_vector
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+tag property
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+tag-model("vectors")
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p
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| A boolean value indicating whether a word vector is associated with the
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| object.
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+aside-code("Example").
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doc = nlp(u'I like apples')
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assert doc.has_vector
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell bool
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+cell Whether the document has a vector data attached.
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+h(2, "vector") Doc.vector
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+tag property
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+tag-model("vectors")
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p
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| A real-valued meaning representation. Defaults to an average of the
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| token vectors.
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+aside-code("Example").
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apples = nlp(u'I like apples')
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assert doc.vector.dtype == 'float32'
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assert doc.vector.shape == (300,)
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell #[code numpy.ndarray[ndim=1, dtype='float32']]
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+cell A 1D numpy array representing the document's semantics.
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+h(2, "vector_norm") Doc.vector_norm
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+tag property
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+tag-model("vectors")
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p
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| The L2 norm of the document's vector representation.
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+aside-code("Example").
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doc1 = nlp(u'I like apples')
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doc2 = nlp(u'I like oranges')
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doc1.vector_norm # 4.54232424414368
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doc2.vector_norm # 3.304373298575751
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assert doc1.vector_norm != doc2.vector_norm
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+table(["Name", "Type", "Description"])
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+footrow
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+cell returns
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+cell float
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+cell The L2 norm of the vector representation.
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+h(2, "attributes") Attributes
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code text]
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+cell unicode
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+cell A unicode representation of the document text.
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+row
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+cell #[code text_with_ws]
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+cell unicode
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+cell
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| An alias of #[code Doc.text], provided for duck-type compatibility
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| with #[code Span] and #[code Token].
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+row
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+cell #[code mem]
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+cell #[code Pool]
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+cell The document's local memory heap, for all C data it owns.
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell The store of lexical types.
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+row
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+cell #[code tensor]
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+cell object
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+cell Container for dense vector representations.
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+row
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+cell #[code user_data]
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+cell -
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+cell A generic storage area, for user custom data.
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+row
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+cell #[code is_tagged]
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+cell bool
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+cell
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| 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.
|