mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 01:46:28 +03:00
569cc98982
* Add load_from_config function * Add train_from_config script * Merge configs and expose via spacy.config * Fix script * Suggest create_evaluation_callback * Hard-code for NER * Fix errors * Register command * Add TODO * Update train-from-config todos * Fix imports * Allow delayed setting of parser model nr_class * Get train-from-config working * Tidy up and fix scores and printing * Hide traceback if cancelled * Fix weighted score formatting * Fix score formatting * Make output_path optional * Add Tok2Vec component * Tidy up and add tok2vec_tensors * Add option to copy docs in nlp.update * Copy docs in nlp.update * Adjust nlp.update() for set_annotations * Don't shuffle pipes in nlp.update, decruft * Support set_annotations arg in component update * Support set_annotations in parser update * Add get_gradients method * Add get_gradients to parser * Update errors.py * Fix problems caused by merge * Add _link_components method in nlp * Add concept of 'listeners' and ControlledModel * Support optional attributes arg in ControlledModel * Try having tok2vec component in pipeline * Fix tok2vec component * Fix config * Fix tok2vec * Update for Example * Update for Example * Update config * Add eg2doc util * Update and add schemas/types * Update schemas * Fix nlp.update * Fix tagger * Remove hacks from train-from-config * Remove hard-coded config str * Calculate loss in tok2vec component * Tidy up and use function signatures instead of models * Support union types for registry models * Minor cleaning in Language.update * Make ControlledModel specifically Tok2VecListener * Fix train_from_config * Fix tok2vec * Tidy up * Add function for bilstm tok2vec * Fix type * Fix syntax * Fix pytorch optimizer * Add example configs * Update for thinc describe changes * Update for Thinc changes * Update for dropout/sgd changes * Update for dropout/sgd changes * Unhack gradient update * Work on refactoring _ml * Remove _ml.py module * WIP upgrade cli scripts for thinc * Move some _ml stuff to util * Import link_vectors from util * Update train_from_config * Import from util * Import from util * Temporarily add ml.component_models module * Move ml methods * Move typedefs * Update load vectors * Update gitignore * Move imports * Add PrecomputableAffine * Fix imports * Fix imports * Fix imports * Fix missing imports * Update CLI scripts * Update spacy.language * Add stubs for building the models * Update model definition * Update create_default_optimizer * Fix import * Fix comment * Update imports in tests * Update imports in spacy.cli * Fix import * fix obsolete thinc imports * update srsly pin * from thinc to ml_datasets for example data such as imdb * update ml_datasets pin * using STATE.vectors * small fix * fix Sentencizer.pipe * black formatting * rename Affine to Linear as in thinc * set validate explicitely to True * rename with_square_sequences to with_list2padded * rename with_flatten to with_list2array * chaining layernorm * small fixes * revert Optimizer import * build_nel_encoder with new thinc style * fixes using model's get and set methods * Tok2Vec in component models, various fixes * fix up legacy tok2vec code * add model initialize calls * add in build_tagger_model * small fixes * setting model dims * fixes for ParserModel * various small fixes * initialize thinc Models * fixes * consistent naming of window_size * fixes, removing set_dropout * work around Iterable issue * remove legacy tok2vec * util fix * fix forward function of tok2vec listener * more fixes * trying to fix PrecomputableAffine (not succesful yet) * alloc instead of allocate * add morphologizer * rename residual * rename fixes * Fix predict function * Update parser and parser model * fixing few more tests * Fix precomputable affine * Update component model * Update parser model * Move backprop padding to own function, for test * Update test * Fix p. affine * Update NEL * build_bow_text_classifier and extract_ngrams * Fix parser init * Fix test add label * add build_simple_cnn_text_classifier * Fix parser init * Set gpu off by default in example * Fix tok2vec listener * Fix parser model * Small fixes * small fix for PyTorchLSTM parameters * revert my_compounding hack (iterable fixed now) * fix biLSTM * Fix uniqued * PyTorchRNNWrapper fix * small fixes * use helper function to calculate cosine loss * small fixes for build_simple_cnn_text_classifier * putting dropout default at 0.0 to ensure the layer gets built * using thinc util's set_dropout_rate * moving layer normalization inside of maxout definition to optimize dropout * temp debugging in NEL * fixed NEL model by using init defaults ! * fixing after set_dropout_rate refactor * proper fix * fix test_update_doc after refactoring optimizers in thinc * Add CharacterEmbed layer * Construct tagger Model * Add missing import * Remove unused stuff * Work on textcat * fix test (again :)) after optimizer refactor * fixes to allow reading Tagger from_disk without overwriting dimensions * don't build the tok2vec prematuraly * fix CharachterEmbed init * CharacterEmbed fixes * Fix CharacterEmbed architecture * fix imports * renames from latest thinc update * one more rename * add initialize calls where appropriate * fix parser initialization * Update Thinc version * Fix errors, auto-format and tidy up imports * Fix validation * fix if bias is cupy array * revert for now * ensure it's a numpy array before running bp in ParserStepModel * no reason to call require_gpu twice * use CupyOps.to_numpy instead of cupy directly * fix initialize of ParserModel * remove unnecessary import * fixes for CosineDistance * fix device renaming * use refactored loss functions (Thinc PR 251) * overfitting test for tagger * experimental settings for the tagger: avoid zero-init and subword normalization * clean up tagger overfitting test * use previous default value for nP * remove toy config * bringing layernorm back (had a bug - fixed in thinc) * revert setting nP explicitly * remove setting default in constructor * restore values as they used to be * add overfitting test for NER * add overfitting test for dep parser * add overfitting test for textcat * fixing init for linear (previously affine) * larger eps window for textcat * ensure doc is not None * Require newer thinc * Make float check vaguer * Slop the textcat overfit test more * Fix textcat test * Fix exclusive classes for textcat * fix after renaming of alloc methods * fixing renames and mandatory arguments (staticvectors WIP) * upgrade to thinc==8.0.0.dev3 * refer to vocab.vectors directly instead of its name * rename alpha to learn_rate * adding hashembed and staticvectors dropout * upgrade to thinc 8.0.0.dev4 * add name back to avoid warning W020 * thinc dev4 * update srsly * using thinc 8.0.0a0 ! Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com> Co-authored-by: Ines Montani <ines@ines.io>
517 lines
17 KiB
Cython
517 lines
17 KiB
Cython
# cython: embedsignature=True
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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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from thinc.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, IS_OOV, PROB
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from .attrs import intify_attrs
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from .errors import Errors, Warnings, user_warning
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memset(&EMPTY_LEXEME, 0, sizeof(LexemeC))
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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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if attr == PROB:
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self.c.prob = value
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elif attr == CLUSTER:
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self.c.cluster = int(value)
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elif isinstance(value, int) or isinstance(value, 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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user_warning(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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def to_bytes(self):
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lex_data = Lexeme.c_to_bytes(self.c)
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start = <const char*>&self.c.flags
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end = <const char*>&self.c.sentiment + sizeof(self.c.sentiment)
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if (end-start) != sizeof(lex_data.data):
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raise ValueError(Errors.E072.format(length=end-start,
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bad_length=sizeof(lex_data.data)))
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byte_string = b"\0" * sizeof(lex_data.data)
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byte_chars = <char*>byte_string
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for i in range(sizeof(lex_data.data)):
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byte_chars[i] = lex_data.data[i]
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if len(byte_string) != sizeof(lex_data.data):
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raise ValueError(Errors.E072.format(length=len(byte_string),
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bad_length=sizeof(lex_data.data)))
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return byte_string
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def from_bytes(self, bytes byte_string):
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# This method doesn't really have a use-case --- wrote it for testing.
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# Possibly delete? It puts the Lexeme out of synch with the vocab.
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cdef SerializedLexemeC lex_data
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if len(byte_string) != sizeof(lex_data.data):
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raise ValueError(Errors.E072.format(length=len(byte_string),
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bad_length=sizeof(lex_data.data)))
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for i in range(len(byte_string)):
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lex_data.data[i] = byte_string[i]
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Lexeme.c_from_bytes(self.c, lex_data)
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self.orth = self.c.orth
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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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return self.c.sentiment
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def __set__(self, float sentiment):
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self.c.sentiment = sentiment
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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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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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return self.c.cluster
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def __set__(self, attr_t x):
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self.c.cluster = 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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return self.c.prob
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def __set__(self, float x):
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self.c.prob = 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.c.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 is_oov:
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"""RETURNS (bool): Whether the lexeme is out-of-vocabulary."""
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def __get__(self):
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return Lexeme.c_check_flag(self.c, IS_OOV)
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def __set__(self, attr_t x):
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Lexeme.c_set_flag(self.c, IS_OOV, x)
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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()`.
|
||
"""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, IS_LOWER)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, IS_LOWER, x)
|
||
|
||
property is_upper:
|
||
"""RETURNS (bool): Whether the lexeme is in uppercase. Equivalent to
|
||
`lexeme.text.isupper()`.
|
||
"""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, IS_UPPER)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, IS_UPPER, x)
|
||
|
||
property is_title:
|
||
"""RETURNS (bool): Whether the lexeme is in titlecase. Equivalent to
|
||
`lexeme.text.istitle()`.
|
||
"""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, IS_TITLE)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, IS_TITLE, x)
|
||
|
||
property is_punct:
|
||
"""RETURNS (bool): Whether the lexeme is punctuation."""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, IS_PUNCT)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, IS_PUNCT, x)
|
||
|
||
property is_space:
|
||
"""RETURNS (bool): Whether the lexeme consist of whitespace characters.
|
||
Equivalent to `lexeme.text.isspace()`.
|
||
"""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, IS_SPACE)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, IS_SPACE, x)
|
||
|
||
property is_bracket:
|
||
"""RETURNS (bool): Whether the lexeme is a bracket."""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, IS_BRACKET)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, IS_BRACKET, x)
|
||
|
||
property is_quote:
|
||
"""RETURNS (bool): Whether the lexeme is a quotation mark."""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, IS_QUOTE)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, IS_QUOTE, x)
|
||
|
||
property is_left_punct:
|
||
"""RETURNS (bool): Whether the lexeme is left punctuation, e.g. )."""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, IS_LEFT_PUNCT)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, IS_LEFT_PUNCT, x)
|
||
|
||
property is_right_punct:
|
||
"""RETURNS (bool): Whether the lexeme is right punctuation, e.g. )."""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, IS_RIGHT_PUNCT)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, IS_RIGHT_PUNCT, x)
|
||
|
||
property is_currency:
|
||
"""RETURNS (bool): Whether the lexeme is a currency symbol, e.g. $, €."""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, IS_CURRENCY)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, IS_CURRENCY, x)
|
||
|
||
property like_url:
|
||
"""RETURNS (bool): Whether the lexeme resembles a URL."""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, LIKE_URL)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, LIKE_URL, x)
|
||
|
||
property like_num:
|
||
"""RETURNS (bool): Whether the lexeme represents a number, e.g. "10.9",
|
||
"10", "ten", etc.
|
||
"""
|
||
def __get__(self):
|
||
return Lexeme.c_check_flag(self.c, LIKE_NUM)
|
||
|
||
def __set__(self, bint x):
|
||
Lexeme.c_set_flag(self.c, LIKE_NUM, x)
|
||
|
||
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)
|