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95 lines
2.2 KiB
ReStructuredText
95 lines
2.2 KiB
ReStructuredText
==============
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The Doc Object
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==============
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.. py:class:: spacy.tokens.doc.Doc
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.. py:method:: __init__(self, Vocab vocab, orths_and_spaces=None)
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:param Vocab vocab: A vocabulary object.
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:param list orths_and_spaces=None: Defaults to None.
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.. py:method:: __getitem__(self, int i)
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:returns: Token
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.. py:method:: __getitem__(self, slice start_colon_end)
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:returns: Span
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.. py:method:: __iter__(self)
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Iterate over tokens
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.. code::
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>>> tokens = nlp(u'Zero one two three four five six')
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>>> tokens[0].orth_
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u'Zero'
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>>> tokens[-1].orth_
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u'six'
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.. py:method:: __len__(self)
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Number of tokens
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.. py:attribute:: sents
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Iterate over sentences in the document.
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:returns generator: Sentences
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.. py:attribute:: ents
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Iterate over named entities in the document.
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:returns tuple: Named Entities
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.. py:attribute:: noun_chunks
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:returns generator:
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.. py:method:: to_array(self, list attr_ids)
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Given a list of M attribute IDs, export the tokens to a numpy ndarray
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of shape N*M, where N is the length of the sentence.
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:param list[int] attr_ids: A list of attribute ID ints.
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:returns feat_array:
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A feature matrix, with one row per word, and one column per attribute
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indicated in the input attr_ids.
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.. py:method:: count_by(self, attr_id)
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Produce a dict of {attribute (int): count (ints)} frequencies, keyed
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by the values of the given attribute ID.
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.. code::
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>>> from spacy.en import English, attrs
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>>> nlp = English()
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>>> tokens = nlp(u'apple apple orange banana')
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>>> tokens.count_by(attrs.ORTH)
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{12800L: 1, 11880L: 2, 7561L: 1}
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>>> tokens.to_array([attrs.ORTH])
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array([[11880],
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[11880],
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[ 7561],
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[12800]])
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.. py:method:: from_array(self, attrs, array)
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.. py:method:: to_bytes(self)
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.. py:method:: from_bytes(self)
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.. py:method:: read_bytes(self)
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.. py:method:: merge(self, int start_idx, int end_idx, unicode tag, unicode lemma, unicode ent_type)
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Merge a multi-word expression into a single token. Currently
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experimental; API is likely to change.
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