mirror of https://github.com/explosion/spaCy.git
137 lines
6.5 KiB
ReStructuredText
137 lines
6.5 KiB
ReStructuredText
===
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API
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===
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.. autoclass:: spacy.en.English
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+-----------+----------------------------------------+-------------+--------------------------+
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| Attribute | Type | Attr API | NoteS |
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+===========+========================================+=============+==========================+
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| strings | :py:class:`strings.StringStore` | __getitem__ | string <-> int mapping |
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+-----------+----------------------------------------+-------------+--------------------------+
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| vocab | :py:class:`vocab.Vocab` | __getitem__ | Look up Lexeme object |
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+-----------+----------------------------------------+-------------+--------------------------+
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| tokenizer | :py:class:`tokenizer.Tokenizer` | __call__ | Get Tokens given unicode |
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+-----------+----------------------------------------+-------------+--------------------------+
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| tagger | :py:class:`en.pos.EnPosTagger` | __call__ | Set POS tags on Tokens |
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+-----------+----------------------------------------+-------------+--------------------------+
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| parser | :py:class:`syntax.parser.GreedyParser` | __call__ | Set parse on Tokens |
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+-----------+----------------------------------------+-------------+--------------------------+
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.. automethod:: spacy.en.English.__call__
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.. autoclass:: spacy.tokens.Tokens
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:members:
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+---------------+-------------+-------------+
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| Attribute | Type | Useful |
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+===============+=============+=============+
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| vocab | Vocab | __getitem__ |
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+---------------+-------------+-------------+
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| vocab.strings | StringStore | __getitem__ |
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+---------------+-------------+-------------+
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Internals
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A Tokens instance stores the annotations in a C-array of TokenC structs.
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Each TokenC struct holds a const pointer to a LexemeC struct, which describes
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a vocabulary item.
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The Token objects are built lazily, from this underlying C-data.
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For faster access, the underlying C data can be accessed from Cython. You
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can also export the data to a numpy array, via Tokens.to_array, if pure Python
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access is required, and you need slightly better performance. However, this
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is both slower and has a worse API than Cython access.
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.. Once a Token object has been created, it is persisted internally in Tokens._py_tokens.
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.. autoclass:: spacy.tokens.Token
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:members:
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+--------------------------------------------------------------------------------+
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| **Context-independent Attributes** (calculated once per orth-value in vocab) |
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+-----------------+-------------+-----------+------------------------------------+
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| Attribute | Type | Attribute | Type |
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+=================+=============+===========+====================================+
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| orth/orth\_ | int/unicode | __len__ | int |
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+-----------------+-------------+-----------+------------------------------------+
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| lower/lower\_ | int/unicode | cluster | int |
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+-----------------+-------------+-----------+------------------------------------+
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| norm/norm\_ | int/unicode | prob | float |
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+-----------------+-------------+-----------+------------------------------------+
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| shape/shape\_ | int/unicode | repvec | ndarray(shape=(300,), dtype=float) |
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+-----------------+-------------+-----------+------------------------------------+
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| prefix/prefix\_ | int/unicode | | |
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+-----------------+-------------+-----------+------------------------------------+
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| suffix/suffix\_ | int/unicode | | |
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+-----------------+-------------+-----------+------------------------------------+
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| **Context-dependent Attributes** (calculated once per token in input) |
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+-----------------+-------------+-----------+------------------------------------+
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| Attribute | Type | Attribute | Type |
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+-----------------+-------------+-----------+------------------------------------+
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| whitespace\_ | unicode | string | unicode |
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+-----------------+-------------+-----------+------------------------------------+
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| pos/pos\_ | int/unicode | dep/dep\_ | int/unicode |
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+-----------------+-------------+-----------+------------------------------------+
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| tag/tag\_ | int/unicode | | |
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+-----------------+-------------+-----------+------------------------------------+
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| lemma/lemma\_ | int/unicode | | |
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+-----------------+-------------+-----------+------------------------------------+
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.. py:class:: vocab.Vocab(self, data_dir=None, lex_props_getter=None)
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.. py:method:: __len__(self) --> int
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.. py:method:: __getitem__(self, id: int) --> unicode
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.. py:method:: __getitem__(self, string: unicode) --> int
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.. py:method:: __setitem__(self, py_str: unicode, props: Dict[str, int[float]) --> None
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.. py:method:: dump(self, loc: unicode) --> None
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.. py:method:: load_lexemes(self, loc: unicode) --> None
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.. py:method:: load_vectors(self, loc: unicode) --> None
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.. py:class:: strings.StringStore(self)
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.. py:method:: __len__(self) --> int
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.. py:method:: __getitem__(self, id: int) --> unicode
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.. py:method:: __getitem__(self, string: bytes) --> id
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.. py:method:: __getitem__(self, string: unicode) --> id
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.. py:method:: dump(self, loc: unicode) --> None
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.. py:method:: load(self, loc: unicode) --> None
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.. py:class:: tokenizer.Tokenizer(self, Vocab vocab, rules, prefix_re, suffix_re, infix_re, pos_tags, tag_names)
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.. py:method:: tokens_from_list(self, List[unicode]) --> spacy.tokens.Tokens
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.. py:method:: __call__(self, string: unicode) --> spacy.tokens.Tokens)
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.. py:attribute:: vocab: spacy.vocab.Vocab
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.. py:class:: en.pos.EnPosTagger(self, strings: spacy.strings.StringStore, data_dir: unicode)
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.. py:method:: __call__(self, tokens: spacy.tokens.Tokens)
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.. py:method:: train(self, tokens: spacy.tokens.Tokens, List[int] golds) --> int
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.. py:method:: load_morph_exceptions(self, exc: Dict[unicode, Dict])
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.. py:class:: syntax.parser.GreedyParser(self, model_dir: unicode)
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.. py:method:: __call__(self, tokens: spacy.tokens.Tokens) --> None
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.. py:method:: train(self, spacy.tokens.Tokens) --> None
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