mirror of
https://github.com/explosion/spaCy.git
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d94e961f14
Fix `Token.is_oov` and `Lexeme.is_oov` so they return `True` when the lexeme does **not** have a vector.
505 lines
16 KiB
Cython
505 lines
16 KiB
Cython
# cython: embedsignature=True
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# coding: utf8
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from __future__ import unicode_literals, print_function
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# Compiler crashes on memory view coercion without this. Should report bug.
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from cython.view cimport array as cvarray
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from libc.string cimport memset
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cimport numpy as np
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np.import_array()
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import numpy
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import warnings
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from thinc.neural.util import get_array_module
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from .typedefs cimport attr_t, flags_t
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from .attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
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from .attrs cimport IS_TITLE, IS_UPPER, LIKE_URL, LIKE_NUM, LIKE_EMAIL, IS_STOP
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from .attrs cimport IS_BRACKET, IS_QUOTE, IS_LEFT_PUNCT, IS_RIGHT_PUNCT
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from .attrs cimport IS_CURRENCY
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from .attrs import intify_attrs
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from .errors import Errors, Warnings
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OOV_RANK = 0xffffffffffffffff # UINT64_MAX
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memset(&EMPTY_LEXEME, 0, sizeof(LexemeC))
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EMPTY_LEXEME.id = OOV_RANK
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cdef class Lexeme:
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"""An entry in the vocabulary. A `Lexeme` has no string context – it's a
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word-type, as opposed to a word token. It therefore has no part-of-speech
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tag, dependency parse, or lemma (lemmatization depends on the
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part-of-speech tag).
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DOCS: https://spacy.io/api/lexeme
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"""
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def __init__(self, Vocab vocab, attr_t orth):
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"""Create a Lexeme object.
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vocab (Vocab): The parent vocabulary
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orth (uint64): The orth id of the lexeme.
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Returns (Lexeme): The newly constructd object.
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"""
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self.vocab = vocab
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self.orth = orth
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self.c = <LexemeC*><void*>vocab.get_by_orth(vocab.mem, orth)
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if self.c.orth != orth:
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raise ValueError(Errors.E071.format(orth=orth, vocab_orth=self.c.orth))
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def __richcmp__(self, other, int op):
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if other is None:
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if op == 0 or op == 1 or op == 2:
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return False
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else:
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return True
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if isinstance(other, Lexeme):
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a = self.orth
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b = other.orth
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elif isinstance(other, long):
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a = self.orth
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b = other
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elif isinstance(other, str):
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a = self.orth_
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b = other
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else:
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a = 0
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b = 1
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if op == 2: # ==
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return a == b
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elif op == 3: # !=
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return a != b
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elif op == 0: # <
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return a < b
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elif op == 1: # <=
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return a <= b
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elif op == 4: # >
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return a > b
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elif op == 5: # >=
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return a >= b
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else:
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raise NotImplementedError(op)
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def __hash__(self):
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return self.c.orth
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def set_attrs(self, **attrs):
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cdef attr_id_t attr
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attrs = intify_attrs(attrs)
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for attr, value in attrs.items():
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# skip PROB, e.g. from lexemes.jsonl
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if isinstance(value, float):
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continue
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elif isinstance(value, (int, long)):
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Lexeme.set_struct_attr(self.c, attr, value)
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else:
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Lexeme.set_struct_attr(self.c, attr, self.vocab.strings.add(value))
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def set_flag(self, attr_id_t flag_id, bint value):
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"""Change the value of a boolean flag.
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flag_id (int): The attribute ID of the flag to set.
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value (bool): The new value of the flag.
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"""
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Lexeme.c_set_flag(self.c, flag_id, value)
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def check_flag(self, attr_id_t flag_id):
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"""Check the value of a boolean flag.
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flag_id (int): The attribute ID of the flag to query.
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RETURNS (bool): The value of the flag.
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"""
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return True if Lexeme.c_check_flag(self.c, flag_id) else False
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def similarity(self, other):
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"""Compute a semantic similarity estimate. Defaults to cosine over
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vectors.
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other (object): The object to compare with. By default, accepts `Doc`,
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`Span`, `Token` and `Lexeme` objects.
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RETURNS (float): A scalar similarity score. Higher is more similar.
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"""
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# Return 1.0 similarity for matches
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if hasattr(other, "orth"):
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if self.c.orth == other.orth:
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return 1.0
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elif hasattr(other, "__len__") and len(other) == 1 \
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and hasattr(other[0], "orth"):
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if self.c.orth == other[0].orth:
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return 1.0
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if self.vector_norm == 0 or other.vector_norm == 0:
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warnings.warn(Warnings.W008.format(obj="Lexeme"))
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return 0.0
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vector = self.vector
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xp = get_array_module(vector)
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return (xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm))
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@property
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def has_vector(self):
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"""RETURNS (bool): Whether a word vector is associated with the object.
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"""
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return self.vocab.has_vector(self.c.orth)
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@property
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def vector_norm(self):
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"""RETURNS (float): The L2 norm of the vector representation."""
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vector = self.vector
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return numpy.sqrt((vector**2).sum())
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property vector:
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"""A real-valued meaning representation.
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RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
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representing the lexeme's semantics.
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"""
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def __get__(self):
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cdef int length = self.vocab.vectors_length
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if length == 0:
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raise ValueError(Errors.E010)
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return self.vocab.get_vector(self.c.orth)
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def __set__(self, vector):
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if len(vector) != self.vocab.vectors_length:
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raise ValueError(Errors.E073.format(new_length=len(vector),
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length=self.vocab.vectors_length))
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self.vocab.set_vector(self.c.orth, vector)
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property rank:
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"""RETURNS (unicode): Sequential ID of the lexemes's lexical type, used
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to index into tables, e.g. for word vectors."""
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def __get__(self):
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return self.c.id
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def __set__(self, value):
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self.c.id = value
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property sentiment:
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"""RETURNS (float): A scalar value indicating the positivity or
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negativity of the lexeme."""
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def __get__(self):
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sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment", {})
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return sentiment_table.get(self.c.orth, 0.0)
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def __set__(self, float x):
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if "lexeme_sentiment" not in self.vocab.lookups:
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self.vocab.lookups.add_table("lexeme_sentiment")
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sentiment_table = self.vocab.lookups.get_table("lexeme_sentiment")
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sentiment_table[self.c.orth] = x
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@property
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def orth_(self):
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"""RETURNS (unicode): The original verbatim text of the lexeme
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(identical to `Lexeme.text`). Exists mostly for consistency with
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the other attributes."""
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return self.vocab.strings[self.c.orth]
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@property
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def text(self):
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"""RETURNS (unicode): The original verbatim text of the lexeme."""
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return self.orth_
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property lower:
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"""RETURNS (unicode): Lowercase form of the lexeme."""
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def __get__(self):
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return self.c.lower
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def __set__(self, attr_t x):
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self.c.lower = x
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property norm:
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"""RETURNS (uint64): The lexemes's norm, i.e. a normalised form of the
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lexeme text.
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"""
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def __get__(self):
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return self.c.norm
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def __set__(self, attr_t x):
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if "lexeme_norm" not in self.vocab.lookups:
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self.vocab.lookups.add_table("lexeme_norm")
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norm_table = self.vocab.lookups.get_table("lexeme_norm")
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norm_table[self.c.orth] = self.vocab.strings[x]
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self.c.norm = x
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property shape:
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"""RETURNS (uint64): Transform of the word's string, to show
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orthographic features.
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"""
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def __get__(self):
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return self.c.shape
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def __set__(self, attr_t x):
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self.c.shape = x
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property prefix:
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"""RETURNS (uint64): Length-N substring from the start of the word.
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Defaults to `N=1`.
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"""
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def __get__(self):
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return self.c.prefix
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def __set__(self, attr_t x):
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self.c.prefix = x
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property suffix:
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"""RETURNS (uint64): Length-N substring from the end of the word.
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Defaults to `N=3`.
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"""
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def __get__(self):
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return self.c.suffix
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def __set__(self, attr_t x):
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self.c.suffix = x
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property cluster:
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"""RETURNS (int): Brown cluster ID."""
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def __get__(self):
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cluster_table = self.vocab.load_extra_lookups("lexeme_cluster")
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return cluster_table.get(self.c.orth, 0)
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def __set__(self, int x):
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cluster_table = self.vocab.load_extra_lookups("lexeme_cluster")
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cluster_table[self.c.orth] = x
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property lang:
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"""RETURNS (uint64): Language of the parent vocabulary."""
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def __get__(self):
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return self.c.lang
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def __set__(self, attr_t x):
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self.c.lang = x
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property prob:
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"""RETURNS (float): Smoothed log probability estimate of the lexeme's
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type."""
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def __get__(self):
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prob_table = self.vocab.load_extra_lookups("lexeme_prob")
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settings_table = self.vocab.load_extra_lookups("lexeme_settings")
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default_oov_prob = settings_table.get("oov_prob", -20.0)
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return prob_table.get(self.c.orth, default_oov_prob)
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def __set__(self, float x):
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prob_table = self.vocab.load_extra_lookups("lexeme_prob")
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prob_table[self.c.orth] = x
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property lower_:
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"""RETURNS (unicode): Lowercase form of the word."""
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def __get__(self):
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return self.vocab.strings[self.c.lower]
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def __set__(self, unicode x):
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self.c.lower = self.vocab.strings.add(x)
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property norm_:
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"""RETURNS (unicode): The lexemes's norm, i.e. a normalised form of the
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lexeme text.
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"""
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def __get__(self):
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return self.vocab.strings[self.c.norm]
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def __set__(self, unicode x):
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self.norm = self.vocab.strings.add(x)
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property shape_:
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"""RETURNS (unicode): Transform of the word's string, to show
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orthographic features.
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"""
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def __get__(self):
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return self.vocab.strings[self.c.shape]
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def __set__(self, unicode x):
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self.c.shape = self.vocab.strings.add(x)
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property prefix_:
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"""RETURNS (unicode): Length-N substring from the start of the word.
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Defaults to `N=1`.
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"""
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def __get__(self):
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return self.vocab.strings[self.c.prefix]
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def __set__(self, unicode x):
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self.c.prefix = self.vocab.strings.add(x)
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property suffix_:
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"""RETURNS (unicode): Length-N substring from the end of the word.
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Defaults to `N=3`.
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"""
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def __get__(self):
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return self.vocab.strings[self.c.suffix]
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def __set__(self, unicode x):
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self.c.suffix = self.vocab.strings.add(x)
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property lang_:
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"""RETURNS (unicode): Language of the parent vocabulary."""
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def __get__(self):
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return self.vocab.strings[self.c.lang]
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def __set__(self, unicode x):
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self.c.lang = self.vocab.strings.add(x)
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property flags:
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"""RETURNS (uint64): Container of the lexeme's binary flags."""
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def __get__(self):
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return self.c.flags
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def __set__(self, flags_t x):
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self.c.flags = x
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@property
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def is_oov(self):
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"""RETURNS (bool): Whether the lexeme is out-of-vocabulary."""
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return self.orth not in self.vocab.vectors
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property is_stop:
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"""RETURNS (bool): Whether the lexeme is a stop word."""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_STOP)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_STOP, x)
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property is_alpha:
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"""RETURNS (bool): Whether the lexeme consists of alphabetic
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characters. Equivalent to `lexeme.text.isalpha()`.
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"""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_ALPHA)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_ALPHA, x)
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property is_ascii:
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"""RETURNS (bool): Whether the lexeme consists of ASCII characters.
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Equivalent to `[any(ord(c) >= 128 for c in lexeme.text)]`.
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"""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_ASCII)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_ASCII, x)
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property is_digit:
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"""RETURNS (bool): Whether the lexeme consists of digits. Equivalent
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to `lexeme.text.isdigit()`.
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"""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_DIGIT)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_DIGIT, x)
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property is_lower:
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"""RETURNS (bool): Whether the lexeme is in lowercase. Equivalent to
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`lexeme.text.islower()`.
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"""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_LOWER)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_LOWER, x)
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property is_upper:
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"""RETURNS (bool): Whether the lexeme is in uppercase. Equivalent to
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`lexeme.text.isupper()`.
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"""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_UPPER)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_UPPER, x)
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property is_title:
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"""RETURNS (bool): Whether the lexeme is in titlecase. Equivalent to
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`lexeme.text.istitle()`.
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"""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_TITLE)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_TITLE, x)
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property is_punct:
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"""RETURNS (bool): Whether the lexeme is punctuation."""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_PUNCT)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_PUNCT, x)
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property is_space:
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"""RETURNS (bool): Whether the lexeme consist of whitespace characters.
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Equivalent to `lexeme.text.isspace()`.
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"""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_SPACE)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_SPACE, x)
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property is_bracket:
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"""RETURNS (bool): Whether the lexeme is a bracket."""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_BRACKET)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_BRACKET, x)
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property is_quote:
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"""RETURNS (bool): Whether the lexeme is a quotation mark."""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_QUOTE)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_QUOTE, x)
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property is_left_punct:
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"""RETURNS (bool): Whether the lexeme is left punctuation, e.g. )."""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_LEFT_PUNCT)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_LEFT_PUNCT, x)
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property is_right_punct:
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"""RETURNS (bool): Whether the lexeme is right punctuation, e.g. )."""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_RIGHT_PUNCT)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_RIGHT_PUNCT, x)
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property is_currency:
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"""RETURNS (bool): Whether the lexeme is a currency symbol, e.g. $, €."""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_CURRENCY)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, IS_CURRENCY, x)
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property like_url:
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"""RETURNS (bool): Whether the lexeme resembles a URL."""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, LIKE_URL)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, LIKE_URL, x)
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property like_num:
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"""RETURNS (bool): Whether the lexeme represents a number, e.g. "10.9",
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"10", "ten", etc.
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"""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, LIKE_NUM)
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def __set__(self, bint x):
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Lexeme.c_set_flag(self.c, LIKE_NUM, x)
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||
|
||
property like_email:
|
||
"""RETURNS (bool): Whether the lexeme resembles an email address."""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, LIKE_EMAIL)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, LIKE_EMAIL, x)
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