mirror of
https://github.com/explosion/spaCy.git
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155 lines
4.9 KiB
Plaintext
155 lines
4.9 KiB
Plaintext
//- ----------------------------------
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//- 💫 DOCS > API > VOCAB
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//- ----------------------------------
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+section("vocab")
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+h(2, "vocab", "https://github.com/" + SOCIAL.github + "/spaCy/blob/master/spacy/vocab.pyx")
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| #[+tag class] Vocab
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p
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| A look-up table that allows you to access #[code.lang-python Lexeme]
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| objects. The #[code.lang-python Vocab] instance also provides access to
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| the #[code.lang-python StringStore], and owns underlying C-data that
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| is shared between #[code.lang-python Doc] objects.
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+aside('Caveat').
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You should avoid working with #[code Doc], #[code Token] or #[code Span]
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objects backed by multiple different #[code Vocab] instances, as
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they may assume inconsistent string-to-integer encodings. All #[code Doc]
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objects produced by the same #[code Language] instance will hold
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a reference to the same #[code Vocab] instance.
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+code("python", "Overview").
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class Vocab:
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StringStore strings
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Morphology morphology
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dict get_lex_attr
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int vectors_length
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def __init__(self, get_lex_attr=None, tag_map=None, lemmatizer=None, serializer_freqs=None):
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return self
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@classmethod
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def load(cls, data_dir, get_lex_attr):
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return Vocab()
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@classmethod
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def from_package(cls, package, get_lx_attr=None, vectors_package=None):
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return Vocab()
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property serializer:
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return Packer()
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def __len__(self):
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return int
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def __contains__(self, string):
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return bool
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def __getitem__(self, id_or_string):
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return Lexeme()
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def dump(self, loc):
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return None
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def load_lexemes(self, loc):
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return None
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def dump_vectors(self, out_loc):
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return None
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def load_vectors(self, file_):
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return int
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def load_vectors_from_bin_loc(self, loc):
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return int
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+table(["Example", "Description"])
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+row
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+cell #[code.lang-python lexeme = vocab[integer_id]]
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+cell.
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Get a lexeme by its orth ID.
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+row
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+cell #[code.lang-python lexeme = vocab[string]]
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+cell.
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Get a lexeme by the string corresponding to its orth ID.
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+row
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+cell #[code.lang-python for lexeme in vocab]
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+cell.
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Iterate over #[code Lexeme] objects.
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+row
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+cell #[code.lang-python int_id = vocab.strings[u'dog']]
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+cell.
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Access the #[code StringStore] via #[code vocab.strings]
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+row
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+cell #[code.lang-python nlp.vocab is nlp.tokenizer.vocab]
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+cell.
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Access the from #[code.lang-python Doc]
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+section("vocab-dump")
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+h(3, "vocab-dump")
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| #[+tag method] Vocab.dump
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+code("python", "Definition").
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def dump(self, loc):
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return None
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+table(["Name", "Type", "Description"])
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+row
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+cell loc
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+cell #[+a(link_unicode) unicode]
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+cell Path where the vocabulary should be saved.
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+section("vocab-load_lexemes")
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+h(3, "vocab-load_lexemes")
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| #[+tag method] Vocab.load_lexemes
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+code("python", "Definition").
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def load_lexemes(self, loc):
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return None
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+table(["Name", "Type", "Description"])
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+row
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+cell loc
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+cell #[+a(link_unicode) unicode]
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+cell Path to load the lexemes.bin file from.
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+section("vocab-dump_vectors")
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+h(3, "vocab-dump_vectors")
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| #[+tag method] Vocab.dump_vectors
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+code("python", "Definition").
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def dump_vectors(self, loc):
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return None
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+section("vocab-loadvectors")
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+h(3, "vocab-loadvectors")
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| #[+tag method] Vocab.load_vectors
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+code("python", "Definition").
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def load_vectors(self, file_):
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return None
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+table(["Name", "Type", "Description"])
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+row
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+cell file
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+cell #[+a(link_unicode) unicode]
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+cell A file-like object, to load word vectors from.
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+section("vocab-loadvectorsfrombinloc")
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+h(3, "vocab-saveload-loadvectorsfrom")
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| #[+tag method] Vocab.load_vectors_from_bin_loc
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+code("python", "Definition").
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def load_vectors_from_bin_loc(self, loc):
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return None
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+table(["Name", "Type", "Description"])
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+row
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+cell loc
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+cell #[+a(link_unicode) unicode]
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+cell.
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A path to a file, in spaCy's binary word-vectors file format.
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