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* Remove the vectors option to Vocab, preferring to either load vectors from disk, or set them on the Lexeme objects.
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@ -137,21 +137,14 @@ class Language(object):
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return path.join(path.dirname(__file__), 'data')
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@classmethod
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def default_vectors(cls, data_dir):
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return None
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@classmethod
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def default_vocab(cls, data_dir=None, get_lex_attr=None, vectors=None):
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def default_vocab(cls, data_dir=None, get_lex_attr=None):
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if data_dir is None:
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data_dir = cls.default_data_dir()
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if vectors is None:
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vectors = cls.default_vectors(data_dir)
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if get_lex_attr is None:
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get_lex_attr = cls.default_lex_attrs(data_dir)
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return Vocab.from_dir(
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path.join(data_dir, 'vocab'),
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get_lex_attr=get_lex_attr,
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vectors=vectors)
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get_lex_attr=get_lex_attr)
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@classmethod
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def default_tokenizer(cls, vocab, data_dir):
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@ -214,7 +207,7 @@ class Language(object):
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self.entity = entity
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self.matcher = matcher
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def __call__(self, text, tag=True, parse=True, entity=True, merge_mwes=False):
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def __call__(self, text, tag=True, parse=True, entity=True):
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"""Apply the pipeline to some text. The text can span multiple sentences,
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and can contain arbtrary whitespace. Alignment into the original string
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is preserved.
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@ -56,11 +56,10 @@ cdef class Vocab:
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self.load_lexemes(path.join(data_dir, 'strings.txt'), path.join(data_dir, 'lexemes.bin'))
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if vectors is None and path.exists(path.join(data_dir, 'vec.bin')):
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self.repvec_length = self.load_rep_vectors(path.join(data_dir, 'vec.bin'))
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self.vectors_length = self.load_vectors(path.join(data_dir, 'vec.bin'))
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return self
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def __init__(self, get_lex_attr=None, tag_map=None, vectors=None, lemmatizer=None,
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serializer_freqs=None):
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def __init__(self, get_lex_attr=None, tag_map=None, lemmatizer=None, serializer_freqs=None):
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if tag_map is None:
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tag_map = {}
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if lemmatizer is None:
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@ -262,7 +261,7 @@ cdef class Vocab:
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i += 1
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fp.close()
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def load_rep_vectors(self, loc):
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def load_vectors(self, loc):
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cdef CFile file_ = CFile(loc, b'rb')
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cdef int32_t word_len
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cdef int32_t vec_len
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