SYMBOL INDEX (154 symbols across 31 files)

FILE: tests/test_general.py
  class StemmerTest (line 14) | class StemmerTest(unittest.TestCase):
    method setUp (line 15) | def setUp(self):
    method test_predict_1 (line 18) | def test_predict_1(self):
    method test_predict_2 (line 32) | def test_predict_2(self):
  class NerTest (line 43) | class NerTest(unittest.TestCase):
    method setUp (line 44) | def setUp(self):
    method test_predict_1 (line 47) | def test_predict_1(self):
  class PoSTest (line 72) | class PoSTest(unittest.TestCase):
    method setUp (line 73) | def setUp(self):
    method test_predict_1 (line 76) | def test_predict_1(self):
  class DependencyParserTest (line 89) | class DependencyParserTest(unittest.TestCase):
    method setUp (line 90) | def setUp(self):
    method test_predict_1 (line 93) | def test_predict_1(self):
  class SentimentAnalyzerTester (line 113) | class SentimentAnalyzerTester(unittest.TestCase):
    method setUp (line 114) | def setUp(self):
    method test_predicts (line 117) | def test_predicts(self):
  class NormalizerTester (line 160) | class NormalizerTester(unittest.TestCase):
    method setUp (line 161) | def setUp(self):
    method test_convert_number_to_words (line 173) | def test_convert_number_to_words(self):
    method test_deasciify (line 195) | def test_deasciify(self):
    method test_misc (line 204) | def test_misc(self):
  class StopwordRemoverTest (line 221) | class StopwordRemoverTest(unittest.TestCase):
    method setUp (line 222) | def setUp(self):
    method test_remove_stopwords (line 225) | def test_remove_stopwords(self):
    method test_dynamic_stopwords (line 233) | def test_dynamic_stopwords(self):

FILE: vnlp/bin/vnlp.py
  function main (line 26) | def main():

FILE: vnlp/dependency_parser/_spu_context_utils.py
  function create_spucontext_dp_model (line 10) | def create_spucontext_dp_model(
  function vectorize_arc_label (line 110) | def vectorize_arc_label(w, arcs, labels, sentence_max_len, tokenizer_lab...
  function process_single_word_input (line 124) | def process_single_word_input(

FILE: vnlp/dependency_parser/_treestack_utils.py
  function create_dependency_parser_model (line 13) | def create_dependency_parser_model(
  function preprocess_word (line 231) | def preprocess_word(word):
  function process_single_word_input (line 238) | def process_single_word_input(
  function convert_numbers_to_zero (line 465) | def convert_numbers_to_zero(text_: str):

FILE: vnlp/dependency_parser/dependency_parser.py
  class DependencyParser (line 7) | class DependencyParser:
    method __init__ (line 17) | def __init__(self, model="SPUContextDP", evaluate=False):
    method predict (line 32) | def predict(
    method __getattr__ (line 84) | def __getattr__(self, name):

FILE: vnlp/dependency_parser/spu_context_dp.py
  class SPUContextDP (line 86) | class SPUContextDP:
    method __init__ (line 96) | def __init__(self, evaluate):
    method predict (line 135) | def predict(

FILE: vnlp/dependency_parser/treestack_dp.py
  class TreeStackDP (line 104) | class TreeStackDP:
    method __init__ (line 116) | def __init__(self, evaluate):
    method predict (line 173) | def predict(

FILE: vnlp/dependency_parser/utils.py
  function dp_pos_to_displacy_format (line 4) | def dp_pos_to_displacy_format(dp_result, pos_result=None):
  function decode_arc_label_vector (line 51) | def decode_arc_label_vector(logits, SENTENCE_MAX_LEN, LABEL_VOCAB_SIZE):

FILE: vnlp/named_entity_recognizer/_charner_utils.py
  function create_charner_model (line 4) | def create_charner_model(

FILE: vnlp/named_entity_recognizer/_spu_context_utils.py
  function create_spucontext_ner_model (line 10) | def create_spucontext_ner_model(
  function process_single_word_input (line 110) | def process_single_word_input(

FILE: vnlp/named_entity_recognizer/charner.py
  class CharNER (line 47) | class CharNER:
    method __init__ (line 61) | def __init__(self, evaluate):
    method _predict_char_level (line 95) | def _predict_char_level(
    method _charner_decoder (line 123) | def _charner_decoder(
    method predict (line 159) | def predict(

FILE: vnlp/named_entity_recognizer/named_entity_recognizer.py
  class NamedEntityRecognizer (line 7) | class NamedEntityRecognizer:
    method __init__ (line 17) | def __init__(self, model="SPUContextNER", evaluate=False):
    method predict (line 32) | def predict(

FILE: vnlp/named_entity_recognizer/spu_context_ner.py
  class SPUContextNER (line 82) | class SPUContextNER:
    method __init__ (line 92) | def __init__(self, evaluate):
    method predict (line 131) | def predict(

FILE: vnlp/named_entity_recognizer/utils.py
  function ner_to_displacy_format (line 4) | def ner_to_displacy_format(text, ner_result):

FILE: vnlp/normalizer/_deasciifier.py
  class Deasciifier (line 5) | class Deasciifier:
    method __init__ (line 13547) | def __init__(self, ascii_string):
    method print_turkish_string (line 13551) | def print_turkish_string(self):
    method set_char_at (line 13554) | def set_char_at(self, mystr, pos, c):
    method convert_to_turkish (line 13557) | def convert_to_turkish(self):
    method turkish_toggle_accent (line 13577) | def turkish_toggle_accent(self, c):
    method turkish_need_correction (line 13606) | def turkish_need_correction(self, char, point=0):
    method turkish_match_pattern (line 13627) | def turkish_match_pattern(self, dlist, point=0):
    method turkish_get_context (line 13649) | def turkish_get_context(self, size=turkish_context_size, point=0):

FILE: vnlp/normalizer/normalizer.py
  class Normalizer (line 9) | class Normalizer:
    method __init__ (line 25) | def __init__(self):
    method lower_case (line 40) | def lower_case(text: str) -> str:
    method remove_punctuations (line 75) | def remove_punctuations(text: str) -> str:
    method remove_accent_marks (line 95) | def remove_accent_marks(text: str) -> str:
    method deasciify (line 130) | def deasciify(tokens: List[str]) -> List[str]:
    method correct_typos (line 156) | def correct_typos(self, text: str) -> str:
    method convert_numbers_to_words (line 184) | def convert_numbers_to_words(
    method _is_token_valid_turkish (line 258) | def _is_token_valid_turkish(self, token):
    method _int_to_words (line 273) | def _int_to_words(self, main_num, put_commas=False):
    method _num_to_words (line 370) | def _num_to_words(self, num, num_dec_digits):

FILE: vnlp/part_of_speech_tagger/_spu_context_utils.py
  function create_spucontext_pos_model (line 10) | def create_spucontext_pos_model(
  function process_single_word_input (line 108) | def process_single_word_input(

FILE: vnlp/part_of_speech_tagger/_treestack_utils.py
  function create_pos_tagger_model (line 10) | def create_pos_tagger_model(
  function preprocess_word (line 185) | def preprocess_word(word):
  function process_single_word_input (line 192) | def process_single_word_input(
  function convert_numbers_to_zero (line 390) | def convert_numbers_to_zero(text_: str):

FILE: vnlp/part_of_speech_tagger/part_of_speech_tagger.py
  class PoSTagger (line 7) | class PoSTagger:
    method __init__ (line 17) | def __init__(self, model="SPUContextPoS", evaluate=False, *args):
    method predict (line 36) | def predict(self, sentence: str) -> List[Tuple[str, str]]:
    method __getattr__ (line 65) | def __getattr__(self, name):

FILE: vnlp/part_of_speech_tagger/spu_context_pos.py
  class SPUContextPoS (line 81) | class SPUContextPoS:
    method __init__ (line 91) | def __init__(self, evaluate):
    method predict (line 130) | def predict(self, sentence: str) -> List[Tuple[str, str]]:

FILE: vnlp/part_of_speech_tagger/treestack_pos.py
  class TreeStackPoS (line 90) | class TreeStackPoS:
    method __init__ (line 101) | def __init__(self, evaluate, stemmer_analyzer=None):
    method predict (line 150) | def predict(self, sentence: str) -> List[Tuple[str, str]]:

FILE: vnlp/sentence_splitter/sentence_splitter.py
  class SentenceSplitter (line 10) | class SentenceSplitter:
    class _PrefixType (line 19) | class _PrefixType(Enum):
    method __init__ (line 23) | def __init__(self):  # ISO 639-1 language code
    method _split (line 51) | def _split(self, text):  # noqa: C901
    method split_sentences (line 185) | def split_sentences(self, text: str) -> List[str]:

FILE: vnlp/sentiment_analyzer/_spu_context_bigru_utils.py
  function create_spucbigru_sentiment_model (line 5) | def create_spucbigru_sentiment_model(
  function process_text_input (line 47) | def process_text_input(text, tokenizer, TEXT_MAX_LEN):

FILE: vnlp/sentiment_analyzer/sentiment_analyzer.py
  class SentimentAnalyzer (line 4) | class SentimentAnalyzer:
    method __init__ (line 14) | def __init__(self, model="SPUCBiGRUSentimentAnalyzer", evaluate=False):
    method predict (line 26) | def predict(self, text: str) -> int:
    method predict_proba (line 52) | def predict_proba(self, text: str) -> float:
    method __getattr__ (line 75) | def __getattr__(self, name):

FILE: vnlp/sentiment_analyzer/spu_context_bigru_sentiment.py
  class SPUCBiGRUSentimentAnalyzer (line 73) | class SPUCBiGRUSentimentAnalyzer:
    method __init__ (line 83) | def __init__(self, evaluate):
    method predict (line 118) | def predict(self, text: str) -> List[Tuple[str, str]]:
    method predict_proba (line 132) | def predict_proba(self, text: str) -> float:

FILE: vnlp/stemmer_morph_analyzer/_melik_utils.py
  function create_stemmer_model (line 5) | def create_stemmer_model(
  function process_input_text (line 186) | def process_input_text(
  function tokenize_stems_tags (line 219) | def tokenize_stems_tags(
  function tokenize_surface_form_context (line 305) | def tokenize_surface_form_context(

FILE: vnlp/stemmer_morph_analyzer/_yildiz_analyzer.py
  class TurkishStemSuffixCandidateGenerator (line 10) | class TurkishStemSuffixCandidateGenerator(object):
    method __init__ (line 57) | def __init__(
    method read_exact_lookup_table (line 73) | def read_exact_lookup_table(self):
    method read_suffix_dic (line 85) | def read_suffix_dic(self):
    method read_stem_list (line 98) | def read_stem_list(self):
    method _parse_flag (line 119) | def _parse_flag(flag):
    method _transform_soft_consonants (line 130) | def _transform_soft_consonants(text):
    method _root_transform (line 144) | def _root_transform(candidate_roots):
    method suffix_transform (line 157) | def suffix_transform(cls, candidate_suffixes):
    method suffix_transform_single (line 164) | def suffix_transform_single(cls, candidate_suffix):
    method _add_candidate_stem_suffix (line 175) | def _add_candidate_stem_suffix(
    method get_stem_suffix_candidates (line 279) | def get_stem_suffix_candidates(self, surface_word):
    method get_tags (line 339) | def get_tags(self, suffix, stem_tags=None):
    method get_analysis_candidates (line 369) | def get_analysis_candidates(self, surface_word):
  function to_lower (line 483) | def to_lower(text):
  function capitalize (line 494) | def capitalize(text):
  function asciify (line 501) | def asciify(text):
  function get_tags_from_analysis (line 517) | def get_tags_from_analysis(analysis):
  function get_root_from_analysis (line 524) | def get_root_from_analysis(analysis):
  function get_pos_from_analysis (line 531) | def get_pos_from_analysis(analysis):
  function get_tags_str_from_analysis (line 538) | def get_tags_str_from_analysis(analysis):
  function standardize_tags (line 545) | def standardize_tags(tags):
  function convert_tag_list_to_str (line 553) | def convert_tag_list_to_str(tags):

FILE: vnlp/stemmer_morph_analyzer/stemmer_morph_analyzer.py
  class StemmerAnalyzer (line 56) | class StemmerAnalyzer:
    method __init__ (line 71) | def __init__(self, evaluate=False):
    method predict (line 116) | def predict(self, sentence: str, batch_size: int = 64) -> List[str]:

FILE: vnlp/stopword_remover/stopword_remover.py
  class StopwordRemover (line 14) | class StopwordRemover:
    method __init__ (line 27) | def __init__(self):
    method dynamically_detect_stop_words (line 33) | def dynamically_detect_stop_words(
    method add_to_stop_words (line 104) | def add_to_stop_words(self, novel_stop_words: List[str]):
    method drop_stop_words (line 120) | def drop_stop_words(self, list_of_tokens: List[str]) -> List[str]:

FILE: vnlp/tokenizer/tokenizer.py
  function WordPunctTokenize (line 5) | def WordPunctTokenize(text: str) -> List[str]:
  function TreebankWordTokenize (line 19) | def TreebankWordTokenize(text: str) -> List[str]:

FILE: vnlp/utils.py
  function check_and_download (line 9) | def check_and_download(file_path, file_url):
  function load_keras_tokenizer (line 27) | def load_keras_tokenizer(tokenizer_json_file_path):
  function create_rnn_stacks (line 40) | def create_rnn_stacks(
  function tokenize_single_word (line 65) | def tokenize_single_word(word, tokenizer_word, TOKEN_PIECE_MAX_LEN):
  function process_word_context (line 76) | def process_word_context(