def main():
if sys.argv.__len__() > 1:
init_dir_name = os.path.normpath(sys.argv[1])
assert os.path.isdir(init_dir_name), 'Directory `{0}` does not exist!'.format(init_dir_name)
all_prompts = sorted(list(get_all_prompts(init_dir_name)))
accentor = Accentor()
morpho_predictor = RNNMorphPredictor()
i = 0
for cur_prompt in all_prompts[:100]:
trouble = False
unknown_words = []
for cur_subsentence in select_subsentences(cur_prompt):
morphotags = ['{0} {1}'.format(cur_morpho.pos, cur_morpho.tag)
for cur_morpho in morpho_predictor.predict_sentence_tags(cur_subsentence)]
accent_variants = accentor.do_accents(cur_subsentence, morphotags)
if len(accent_variants) > 1:
trouble = True
else:
accented_phrase = accent_variants[0]
for cur_word in accented_phrase:
vowels_counter = 0
for cur_char in cur_word.lower():
if cur_char in VOWEL_LETTERS:
vowels_counter += 1
if '+' not in cur_word and vowels_counter > 1:
unknown_words += [cur_word]
if trouble:
print('`{0}`: this phrase cannot be unambiguously accented!'.format(cur_prompt))
i += 1
if unknown_words:
for unknown_word in list(set(unknown_words)):
print('`{0}`: word `{1}` in this this phrase is unknown!'.format(cur_prompt, unknown_word))
print(i)
else:
print("Usage: input_directory_with_voxforge_ru")
def __init__(self, batch_size=1):
"""[summary]
Args:
batch_size (int, optional): [description]. Defaults to 1.
"""
self.batch_size = batch_size
self.predictor = RNNMorphPredictor(language="ru")
def __init__(self):
self._graph = tf.Graph()
self._session = tf.Session(graph=self._graph)
with self._session.as_default():
with self._graph.as_default():
self.rnnmorph = RNNMorphPredictor(language="ru")
self.pymorphy_analyzer = pymorphy2.MorphAnalyzer()
self.latin = re.compile("^[0-9]*[A-Za-z]+[0-9]*$")
self.cyrillic = re.compile("[А-Яа-яЁе]+")
def prepare_text(text):
"""
"""
words = [
w for w in nltk.word_tokenize(text, language="russian")
if w not in punctuation
]
predictor = RNNMorphPredictor(language="ru")
morphs = predictor.predict(words)
return ["{}_{}".format(m.normal_form, m.pos) for m in morphs]
class MorphPredictor(PreProcesser):
def __init__(self):
self.rnnmorph = RNNMorphPredictor(language='ru')
def translit(self, form):
return (True, translit(form, 'ru')) if re.match(r'[a-zA-Z]+', form) else (False, form)
def transform_sent(self, sent):
sent = sent.copy()
translit_flags, translit_forms = zip(*[self.translit(token.form) for token in sent.tokens])
morth_forms = self.rnnmorph.predict(translit_forms)
for token, morth_form, translit_flag in zip(sent.tokens, morth_forms, translit_flags):
token.lemma = token.form.lower() if translit_flag else morth_form.normal_form
token.upos = morth_form.pos
token.feats = morth_form.tag
return sent
def transform_item(self, x):
return [self.transform_sent(sent) for sent in x]
def pos_tag(self):
if self.language == "ru" or self.language == "en":
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
predictor = RNNMorphPredictor(language=self.language)
sentences = []
for review in self.reviews:
for i, sentence in enumerate(review.sentences):
words = [word.text for word in sentence]
sentences.append(words)
sentences_forms = predictor.predict_sentences(sentences, 32, False)
offset = 0
for review in self.reviews:
for i, sentence in enumerate(review.sentences):
forms = sentences_forms[offset + i]
for word_idx, form in enumerate(forms):
sentence[word_idx] = PosTaggedWord(
sentence[word_idx], form.pos, form.tag,
[int(j) for j in form.vector])
offset += len(review.sentences)
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def handle_new_messages(self):
pr = RNNMorphPredictor()
for collection_name in self.db.collection_names():
if collection_name.startswith('Chat'):
print('Обрабатываем сообщения чата', collection_name)
new_messages = self.db[collection_name].find(
{'handled': {
"$exists": False
}})
for message in new_messages:
handled_data = self.handle_message(message, pr)
self.db[collection_name].update_one(
{'_id': message['_id']}, {'$set': handled_data})
def handle_new_messages(self):
pr = RNNMorphPredictor()
self.mycursor.execute(
"SELECT id, text FROM messages WHERE handled IS NULL")
message_records = self.mycursor.fetchall()
emoticons_count = 0
words_count = 0
for message_record in message_records:
msg_id, text = message_record
if text:
emoticons = regex.findall(r'\X', text)
for emoticon in emoticons:
if any(char in emoji.UNICODE_EMOJI for char in emoticon):
self.mycursor.execute(
f"INSERT INTO emoticons (message_id, emoticon) VALUES (%s, %s);",
(msg_id, emoticon))
emoticons_count += 1
sentences = []
for sentence in re.split(r'[.!?]+', re.sub(r'[ёЁ]', 'е',
text)):
word_list = re.findall(
r'[а-яА-ЯёЁ]+-[а-яА-ЯёЁ]+|[а-яА-ЯёЁ]+', sentence)
if word_list:
sentences.append(word_list)
if sentences:
pr_sentences = pr.predict_sentences(sentences=sentences)
for pr_sentence in pr_sentences:
for pr_word in pr_sentence:
self.mycursor.execute(
f"INSERT INTO words (message_id, word, normal_form, pos, tag) "
f"VALUES (%s, %s, %s, %s, %s);",
(msg_id, pr_word.word, pr_word.normal_form,
pr_word.pos, pr_word.tag))
words_count += 1
self.mycursor.execute(
"UPDATE messages SET handled = %s WHERE id = %s",
(True, message_record[0]))
self.mydb.commit()
return len(message_records), words_count, emoticons_count
def main():
try:
log('Loading predictor')
global PREDICTOR
PREDICTOR = RNNMorphPredictor(language="ru")
except Exception as error:
log('Can not load analyzer: "%s"', error)
return
server = HTTPServer((HOST, PORT), HTTPHandler)
try:
log('Listening http://%s:%d', HOST, PORT)
server.serve_forever()
except KeyboardInterrupt:
log('Quiting')
finally:
server.server_close()
def __process_line(line: str, output_file: TextIO,
sentence_splitter: SentenceSplitter,
morph_predictor: RNNMorphPredictor):
sentences = sentence_splitter.split(line)
for sentence in sentences:
words = [
token.text for token in Tokenizer.tokenize(sentence) if
token.text != '' and token.token_type != Token.TokenType.SPACE
]
if not words:
continue
forms = morph_predictor.predict_sentence_tags(words)
for form in forms:
if form.pos == "PUNCT":
continue
output_file.write(
"%s\t%s\t%s\t%s\n" %
(form.word, form.normal_form, form.pos, form.tag))
output_file.write("\n")
def find_rhyme(src: str, russian_lexemes: dict, rnn_morph: RNNMorphPredictor,
phonetic_dict: Dict[str, tuple]) -> List[str]:
russian_letters = set('АаБбВвГгДдЕеЁёЖжЗзИиЙйКкЛлМмНнОоПпРрСсТтУуФфХхЦцЧчШшЩщЪъЫыЬьЭэЮюЯя')
src_words = list(filter(
lambda it2: set(it2) <= russian_letters,
map(lambda it1: it1.strip().lower(), word_tokenize(src))
))
if len(src_words) == 0:
return [src]
morphotags = [get_morphodata(cur.pos + ' ' + cur.tag) for cur in rnn_morph.predict(src_words)]
print('morphotags', morphotags)
syllables_of_words = [str(calc_number_of_syllables(cur_word)) for cur_word in src_words]
print('syllables_of_words', syllables_of_words)
variants = []
new_variant = []
for it in select_new_variant(src_words, morphotags, syllables_of_words, russian_lexemes, phonetic_dict, 0,
new_variant):
variants.append(' '.join(it))
del it
return variants
def get_morph_markup(input_filenames: List[str], output_filename: str):
"""
Разметка по грамматическим значениям
:param input_filenames: входные текстовые файлы
:param output_filename: путь к файлу, куда будет сохранена разметка
"""
if os.path.exists(output_filename):
os.remove(output_filename)
sentence_splitter = SentenceSplitter(language='ru')
morph_predictor = RNNMorphPredictor()
for filename in input_filenames:
with open(filename, "r",
encoding="utf-8") as r, open(output_filename,
"w+",
encoding="utf-8") as w:
for line in r:
Morph.__process_line(line, w, sentence_splitter,
morph_predictor)
class Preprocessor():
def __init__(self):
self.predictor = RNNMorphPredictor()
def gettags(self, text):
analysis = self.predictor.predict_sentence_tags(text)
words_and_tags = []
for word in analysis:
word_and_tag = []
word_and_tag.append(word.word)
word_and_tag.append(word.pos + ' ' + word.tag)
words_and_tags.append(word_and_tag)
return words_and_tags
def preprocessing(self, text):
text = sub('[\.\,\?\!\(\);:]+', ' <sil>', text)
text = sub(' [–-] |\n', ' <sil> ', text)
text = sub('\s{2,}', ' ', text)
text = sub('^\s|[\\\/@#~¬`£€\$%\^\&\*–_=+\'\"\|«»–-]+', '', text)
words_and_tags = self.gettags(text.split(' '))
return words_and_tags
def tag(predictor: RNNMorphPredictor, untagged_filename: str,
tagged_filename: str):
sentences = []
with open(untagged_filename, "r", encoding='utf-8') as r:
words = []
for line in r:
if line != "\n":
records = line.strip().split("\t")
word = records[1]
words.append(word)
else:
sentences.append([word.lower() for word in words])
words = []
with open(tagged_filename, "w", encoding='utf-8') as w:
all_forms = predictor.predict_sentences_tags(sentences)
for forms in all_forms:
for i, form in enumerate(forms):
line = "{}\t{}\t{}\t{}\t{}\n".format(str(i + 1), form.word,
form.normal_form,
form.pos, form.tag)
w.write(line)
w.write("\n")
class TaggerEnsemble:
def __init__(self):
self.predictor = RNNMorphPredictor(language="ru")
self.tagger = rupostagger.RuPosTagger()
self.tagger.load()
#model_file = '/home/inkoziev/polygon/GramEval2020/tmp/udpipe_syntagrus.model'
#self.ud_model = Model.load(model_file)
#self.ud_pipeline = Pipeline(self.ud_model, 'vertical', Pipeline.DEFAULT, Pipeline.DEFAULT, 'conllu')
#self.ud_error = ProcessingError()
def tag(self, words):
tokens1 = self.tagger.tag(words)
tokens2 = self.predictor.predict(words)
#processed = self.ud_pipeline.process('\n'.join(words), self.ud_error)
#if self.ud_error.occurred():
# print("An error occurred when running run_udpipe: ")
# print(self.ud_error.message)
# return tokens1
#tokens3 = pyconll.load_from_string(processed)[0]
new_tokens = []
for token1, token2 in zip(tokens1, tokens2):
tags1 = token1[1].split('|')
if tags1[0] == 'NOUN' and 'Case' in token2.tag:
tags_rnn = dict(
z.split('=') for z in token2.tag.split('|') if '=' in z)
new_tagset = list(
filter(lambda z: not z.startswith('Case'), tags1))
new_tagset.append(('Case=' + tags_rnn['Case']))
new_tokens.append((token1[0], '|'.join(new_tagset)))
else:
new_tokens.append(token1)
return new_tokens
def __init__(self):
self.predictor = RNNMorphPredictor()
#!/usr/bin/env python
# coding: utf-8
from nltk import sent_tokenize, word_tokenize
import ufal.udpipe
from rnnmorph.predictor import RNNMorphPredictor
from udpipe_model import Model
predictor = RNNMorphPredictor(language="ru")
# Download model from https://rusvectores.org/static/models/udpipe_syntagrus.model
model_file = 'udpipe_syntagrus.model'
model = Model(model_file)
def to_conllu(wordforms):
lines = []
for i in range(len(wordforms)):
line = [
str(i + 1), wordforms[i].word, wordforms[i].normal_form,
wordforms[i].pos, wordforms[i].tag
]
lines.append('\t'.join(line + ['_'] * 5))
return '\n'.join(lines)
def pipeline(sentence):
tokens = word_tokenize(sentence)
forms = predictor.predict(tokens)
sentences = model.read(to_conllu(forms), 'conllu')
for s in sentences:
def __init__(self):
self.rnnmorph = RNNMorphPredictor(language='ru')
def setUpClass(cls):
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
cls.predictor = RNNMorphPredictor()
def __init__(self):
self.predictor = RNNMorphPredictor(language="ru")
self.tagger = rupostagger.RuPosTagger()
self.tagger.load()
# In[33]:
true_pred = sum((1 for t1, t2 in zip(nltk_result, test_sents)
for tt1, tt2 in zip(t1, t2) if tt1[1] == tt2[1]))
num_pred = sum(1 for s in test_sents for _ in s)
print(f"{true_pred / num_pred * 100:.1f}")
# In[34]:
get_ipython().run_cell_magic('capture', '', '!pip install -q rnnmorph')
# In[35]:
from rnnmorph.predictor import RNNMorphPredictor
predictor = RNNMorphPredictor(language="en")
# In[36]:
rnnmorph_result = predictor.predict_sentences(
[list(map(lambda t: t[0], s)) for s in test_sents])
# In[37]:
true_pred = sum((1 for t1, t2 in zip(rnnmorph_result, test_sents)
for tt1, tt2 in zip(t1, t2) if tt1.pos == tt2[1]))
num_pred = sum(1 for s in test_sents for _ in s)
print(f"{true_pred / num_pred * 100:.1f}")
# ### Вопрос 7:
# * Какое качество вы получили, используя каждую из двух библиотек? Сравните их результаты.
class Preprocessor():
def __init__(self, batch_size=1):
self.batch_size = batch_size
self.predictor = RNNMorphPredictor(language="ru")
def __del__(self):
if hasattr(self, 'predictor'):
del self.predictor
def __copy__(self):
cls = self.__class__
result = cls.__new__(cls)
result.predictor = self.predictor
return result
def __deepcopy__(self, memodict={}):
cls = self.__class__
result = cls.__new__(cls)
result.predictor = self.predictor
return result
def gettags(self, texts):
if not isinstance(texts, list):
raise ValueError('Expected `{0}`, but got `{1}`.'.format(
type([1, 2]), type(texts)))
if len(texts) == 0:
return []
all_phonetic_phrases = []
all_phrases_for_rnnmorph = []
for cur_text in texts:
list_of_phonetic_phrases = [
cur.strip() for cur in ' '.join(cur_text).split('<sil>')
]
united_phrase_for_rnnmorph = []
for phonetic_phrase in list_of_phonetic_phrases:
if len(phonetic_phrase) > 0:
united_phrase_for_rnnmorph += phonetic_phrase.split()
if len(united_phrase_for_rnnmorph) > 0:
all_phrases_for_rnnmorph.append(united_phrase_for_rnnmorph)
all_phonetic_phrases.append(list_of_phonetic_phrases)
else:
all_phonetic_phrases.append([])
if len(all_phrases_for_rnnmorph) > 0:
all_forms = self.predictor.predict_sentences(
all_phrases_for_rnnmorph, batch_size=self.batch_size)
else:
all_forms = []
all_words_and_tags = []
phrase_ind = 0
for cur in all_phonetic_phrases:
words_and_tags = [['<sil>', 'SIL _']]
if len(cur) > 0:
token_ind = 0
for phonetic_phrase in cur:
if len(phonetic_phrase) > 0:
n = len(phonetic_phrase.split(' '))
analysis = all_forms[phrase_ind][token_ind:(token_ind +
n)]
for word in analysis:
word_and_tag = []
word_and_tag.append(word.word)
word_and_tag.append(word.pos + ' ' + word.tag)
words_and_tags.append(word_and_tag)
words_and_tags.append(['<sil>', 'SIL _'])
token_ind += n
phrase_ind += 1
all_words_and_tags.append(words_and_tags)
return all_words_and_tags
def preprocessing(self, texts):
def prepare(src):
dst = sub('[\.\,\?\!\(\);:]+', ' <sil>', src.lower())
dst = sub(' [–-] |\n', ' <sil> ', dst)
dst = sub('\s{2,}', ' ', dst)
dst = sub('^\s|(?<!\w)[\\\/@#~¬`£€\$%\^\&\*–_=+\'\"\|«»–-]+', '',
dst)
return dst.strip().split(' ')
words_and_tags = self.gettags([prepare(cur) for cur in texts])
return words_and_tags
class Preprocessor():
"""[summary]
"""
def __init__(self, batch_size=1):
"""[summary]
Args:
batch_size (int, optional): [description]. Defaults to 1.
"""
self.batch_size = batch_size
self.predictor = RNNMorphPredictor(language="ru")
def __del__(self):
if hasattr(self, 'predictor'):
del self.predictor
def __copy__(self):
cls = self.__class__
result = cls.__new__(cls)
result.predictor = self.predictor
return result
def __deepcopy__(self, memodict={}):
cls = self.__class__
result = cls.__new__(cls)
result.predictor = self.predictor
return result
def gettags(self, texts: list) -> list:
"""Get morpho tags for the `texts`
Args:
texts (list): List of lists
Raises:
ValueError: [description]
Returns:
list: list of lists -- words and motpho tags
Example:
PreProcess.gettags([['я купил самолёт и ракеты'], ['ух ты']])
[[['<sil>', 'SIL _'],
['я', 'PRON Case=Nom|Number=Sing|Person=1'],
['купил', 'VERB Gender=Masc|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act'],
['самолёт', 'NOUN Case=Acc|Gender=Masc|Number=Sing'],
['и', 'CONJ _'],
['ракеты', 'NOUN Case=Acc|Gender=Fem|Number=Plur'],
['<sil>', 'SIL _']],
[['<sil>', 'SIL _'],
['ух', 'INTJ _'],
['ты', 'PRON Case=Nom|Number=Sing|Person=2'],
['<sil>', 'SIL _']]]
"""
if not isinstance(texts, list):
raise ValueError(
f'Expected `{type([1, 2])}`, but got `{type(texts)}`.')
if len(texts) == 0:
return []
all_phonetic_phrases = []
all_phrases_for_rnnmorph = []
for cur_text in texts:
list_of_phonetic_phrases = [
cur.strip() for cur in ' '.join(cur_text).split('<sil>')
]
united_phrase_for_rnnmorph = []
for phonetic_phrase in list_of_phonetic_phrases:
if len(phonetic_phrase) > 0:
united_phrase_for_rnnmorph += phonetic_phrase.split()
if len(united_phrase_for_rnnmorph) > 0:
all_phrases_for_rnnmorph.append(united_phrase_for_rnnmorph)
all_phonetic_phrases.append(list_of_phonetic_phrases)
else:
all_phonetic_phrases.append([])
if len(all_phrases_for_rnnmorph) > 0:
all_forms = self.predictor.predict_sentences(all_phrases_for_rnnmorph, \
batch_size=self.batch_size)
else:
all_forms = []
all_words_and_tags = []
phrase_ind = 0
for cur in all_phonetic_phrases:
words_and_tags = [['<sil>', 'SIL _']]
if len(cur) > 0:
token_ind = 0
for phonetic_phrase in cur:
if len(phonetic_phrase) > 0:
n = len(phonetic_phrase.split(' '))
analysis = all_forms[phrase_ind][token_ind:(token_ind +
n)]
for word in analysis:
word_and_tag = []
word_and_tag.append(word.word)
word_and_tag.append(word.pos + ' ' + word.tag)
words_and_tags.append(word_and_tag)
words_and_tags.append(['<sil>', 'SIL _'])
token_ind += n
phrase_ind += 1
all_words_and_tags.append(words_and_tags)
return all_words_and_tags
def __call__(self, texts: str):
"""Call the instance like function. Use in pipelines, too."""
return self.preprocessing(texts)[0]
def preprocessing(self, texts: str):
"""[summary]
Args:
texts (str): Text to preprocess.
Returns:
list: A list of processed words and tags.
"""
def prepare(text: str) -> str:
"""Replace punctuation marks with <sil> tag; remove special symbols."""
text = sub(r'[\.\,\?\!\(\);:]+', ' <sil>', text.lower())
text = sub(r' [–-] |\n', ' <sil> ', text)
text = sub(r'\s{2,}', ' ', text)
text = sub(r'^\s|(?<!\w)[\\\/@#~¬`£€\$%\^\&\*–_=+\'\"\|«»–-]+', '',
text)
return text.strip().split(' ')
return self.gettags([prepare(cur) for cur in texts])
from rnnmorph.predictor import RNNMorphPredictor
from pprint import pprint
if __name__ == '__main__':
pr = RNNMorphPredictor(language='ru')
forms = pr.predict(words=['мама', 'мыла', 'раму'])
for i in forms:
print('{:<15} {:<10} {}'.format(i.normal_form, i.pos, i.tag))
forms = pr.predict_sentences(sentences=[['Всем', 'привет']])
for i in forms[0]:
print('{:<15} {:<10} {}'.format(i.normal_form, i.pos, i.tag))
pprint(forms)
class RNNMorphWrapper:
"""
Класс предназначен для получения граммемной информации о токенах.
"""
def __init__(self):
self._graph = tf.Graph()
self._session = tf.Session(graph=self._graph)
with self._session.as_default():
with self._graph.as_default():
self.rnnmorph = RNNMorphPredictor(language="ru")
self.pymorphy_analyzer = pymorphy2.MorphAnalyzer()
self.latin = re.compile("^[0-9]*[A-Za-z]+[0-9]*$")
self.cyrillic = re.compile("[А-Яа-яЁе]+")
def _choose_pymorphy_form(self, word, lemma, pos):
hypotheses = self.pymorphy_analyzer.parse(word)
hyp = None
tags_to_add = {}
other = ""
for hyp in hypotheses:
if hyp.normal_form == lemma:
break
changed_lemma = lemma.replace("ё", "е")
if not hyp:
return other, tags_to_add, changed_lemma
str_tag = str(hyp.tag)
if "Surn" in str_tag:
other = "фам"
changed_lemma = word.lower().replace("ё", "е")
elif "Patr" in str_tag:
other = "отч"
changed_lemma = word.lower().replace(
"ё", "е") # у Петрович лемма внезапно Пётр
if hyp.tag.transitivity:
tags_to_add[TRANSITIVITY] = str(hyp.tag.transitivity)
if hyp.tag.animacy and pos == "NOUN":
tags_to_add[ANIMACY] = str(hyp.tag.animacy)
if hyp.tag.aspect:
tags_to_add[ASPECT] = str(hyp.tag.aspect)
return other, tags_to_add, changed_lemma
def _change_pos(self, token, analysis):
if re.match(self.latin, analysis.word):
token[GRAMMEM_INFO][PART_OF_SPEECH] = "X"
elif analysis.pos == "PUNCT" and re.search(self.cyrillic,
analysis.word):
token[GRAMMEM_INFO][PART_OF_SPEECH] = "X"
else:
token[GRAMMEM_INFO][PART_OF_SPEECH] = analysis.pos
return token
def _gram_info_processing(self, tags_to_add, analysis):
gramme_info = {}
raw_gram_data = []
if analysis.tag != "_":
for tag in analysis.tag.split("|"):
gramme_info[tag.split("=")[0].lower()] = tag.split(
"=")[1].lower()
gramme_info.update(tags_to_add)
sorted_gramme_info = {
key: gramme_info[key]
for key in sorted(gramme_info.keys())
}
for key in sorted_gramme_info:
raw_gram_data.append(key + "=" + sorted_gramme_info[key])
raw_gram_info = "|".join(raw_gram_data)
return sorted_gramme_info, raw_gram_info
def _rnnmorph_to_token_dicti(self, token, analysis):
additional_info, tags_to_add, changed_lemma = self._choose_pymorphy_form(
analysis.word, analysis.normal_form, analysis.pos)
sorted_gramme_info, raw_gram_info = self._gram_info_processing(
tags_to_add, analysis)
token[GRAMMEM_INFO] = sorted_gramme_info
token[GRAMMEM_INFO][RAW_GRAM_INFO] = raw_gram_info
if additional_info:
token[GRAMMEM_INFO][OTHER] = additional_info
token = self._change_pos(token, analysis)
token[LEMMA] = changed_lemma
return token
def token_desc_list_processing(self, token_desc_list):
"""
Получить список токенов с описанием
:param: Список из словарей
:return: Список из словарей, обогащенный морфологической информацией
"""
raw_token_list = [token[TEXT] for token in token_desc_list]
with self._session.as_default():
with self._graph.as_default():
analyze_result = self.rnnmorph.predict(raw_token_list)
res = []
for i in range(len(token_desc_list)):
analysis = analyze_result[i]
tokenized_element = token_desc_list[i]
final_tokenized_element = self._rnnmorph_to_token_dicti(
tokenized_element, analysis)
res.append(final_tokenized_element)
return res
def __call__(self, token_desc_list):
"""
Класс предназначен для забора из RNNMorph + pymorphy2 граммемной информации.
На вход принимается список токенов
На выходе имеем список токенов с проставленными грамматическими атрибутами
:param token_desc_list (list of dicts)
:return: final_result (enriched list of dicts)
"""
final_result = []
sentences = token_list_to_sentences(token_desc_list)
for sentence in sentences:
final_result.extend(self.token_desc_list_processing(sentence))
if final_result:
final_result.append({
TEXT:
".",
LEMMA:
".",
TOKEN_TYPE:
SENTENCE_ENDPOINT_TOKEN,
TOKEN_VALUE: {
VALUE: "."
},
LIST_OF_TOKEN_TYPES_DATA: [{
TOKEN_TYPE: SENTENCE_ENDPOINT_TOKEN,
TOKEN_VALUE: {
VALUE: "."
}
}]
})
return final_result
def __init__(self, batch_size=1):
self.batch_size = batch_size
self.predictor = RNNMorphPredictor(language="ru")
def get_morph_predictor():
""" """
return RNNMorphPredictor(language="ru")
