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417 lines
10 KiB
Plaintext
417 lines
10 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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+h(2, "attributes") Attributes
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+table(["Name", "Type", "Description"])
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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 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
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| tagged.
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+row
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+cell #[code is_parsed]
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+cell bool
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+cell A flag indicating that the document has been syntactically parsed.
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+row
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+cell #[code sentiment]
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+cell float
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+cell The document's positivity/negativity score, if available.
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+row
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+cell #[code user_hooks]
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+cell dict
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+cell
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| A dictionary that allows customisation of the #[code Doc]'s
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| properties.
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+row
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+cell #[code user_token_hooks]
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+cell dict
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+cell
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| A dictionary that allows customisation of properties of
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| #[code Token] chldren.
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+row
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+cell #[code user_span_hooks]
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+cell dict
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+cell
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| A dictionary that allows customisation of properties of
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| #[code Span] chldren.
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+h(2, "init") Doc.__init__
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+tag method
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p Construct a #[code Doc] object.
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+aside("Note")
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| The most common way to get a #[code Doc] object is via the #[code nlp]
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| object. This method is usually only used for deserialization or preset
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| tokenization.
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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 return
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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 Get a #[code Token] object.
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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 return
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+cell #[code Token]
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+cell The token at #[code doc[i]].
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p Get a #[code Span] object.
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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 return
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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 Iterate over #[code Token] objects.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yield
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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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+table(["Name", "Type", "Description"])
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+footrow
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+cell return
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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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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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+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 return
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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, "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 import attrs
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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([attrs.LOWER, attrs.POS,
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attrs.ENT_TYPE, attrs.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 return
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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, "count_by") Doc.count_by
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+tag method
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p Count the frequencies of a given attribute.
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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 return
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+cell dict
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+cell A dictionary mapping attributes to integer counts.
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+h(2, "from_array") Doc.from_array
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+tag method
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p Load attributes from a numpy array.
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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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+row
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+cell #[code values]
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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 return
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+cell #[code None]
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+cell -
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+h(2, "to_bytes") Doc.to_bytes
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+tag method
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p Export the document contents to a binary string.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell return
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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 Import the document contents from a binary string.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code byte_string]
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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 return
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+cell #[code Doc]
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+cell The #[code self] variable.
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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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+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 return
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+cell #[code Token]
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+cell
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| The newly merged token, or None if the start and end
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| indices did not fall at token boundaries
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+h(2, "read_bytes") Doc.read_bytes
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+tag staticmethod
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p A static method, used to read serialized #[code Doc] objects from a file.
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+aside-code("Example").
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from spacy.tokens.doc import Doc
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loc = 'test_serialize.bin'
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with open(loc, 'wb') as file_:
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file_.write(nlp(u'This is a document.').to_bytes())
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file_.write(nlp(u'This is another.').to_bytes())
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docs = []
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with open(loc, 'rb') as file_:
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for byte_string in Doc.read_bytes(file_):
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docs.append(Doc(nlp.vocab).from_bytes(byte_string))
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assert len(docs) == 2
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+table(["Name", "Type", "Description"])
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+row
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+cell file
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+cell buffer
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+cell A binary buffer to read the serialized annotations from.
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+footrow
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+cell yield
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+cell bytes
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+cell Binary strings from with documents can be loaded.
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+h(2, "text") Doc.text
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+tag property
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p A unicode representation of the document text.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell return
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+cell unicode
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+cell The original verbatim text of the document.
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+h(2, "text_with_ws") Doc.text_with_ws
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+tag property
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p
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| An alias of #[code Doc.text], provided for duck-type compatibility with
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| #[code Span] and #[code Token].
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+table(["Name", "Type", "Description"])
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+footrow
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+cell return
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+cell unicode
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+cell The original verbatim text of the document.
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+h(2, "sents") Doc.sents
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+tag property
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p Iterate over the sentences in the document.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yield
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+cell #[code Span]
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+cell Sentences in the document.
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+h(2, "ents") Doc.ents
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+tag property
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p Iterate over the entities in the document.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yield
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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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p
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| Iterate over the base noun phrases in the document. A base noun phrase,
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| or "NP chunk", is a noun phrase that does not permit other NPs to be
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| nested within it.
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+table(["Name", "Type", "Description"])
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+footrow
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+cell yield
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+cell #[code Span]
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+cell Noun chunks in the document
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+h(2, "vector") Doc.vector
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+tag property
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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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+table(["Name", "Type", "Description"])
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+footrow
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+cell return
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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, "has_vector") Doc.has_vector
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+tag property
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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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+table(["Name", "Type", "Description"])
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+footrow
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+cell return
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+cell bool
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+cell Whether the document has a vector data attached.
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