def train_all_models(filename):
sentences = loadGIKRYGrammasFromFile(filename)
predictors = [
'pos',
'gender',
'case',
'nounType',
'fullForm',
'aspect',
'aspectual',
'tense',
'verbForm',
'trans',
'categoryOfAdjective',
'syntaxType',
'typeOfAnother',
'typeOfAdposition',
'structureOfAdposition',
'categoryOfNumeral',
'formOfNumeral',
]
for predictor in predictors:
print("start to train %s predictor" % predictor)
trainer = Trainer(verbose=True)
features_builder = FeaturesBuilder(predictor)
for sentence in sentences:
(features,
results) = features_builder.make_features_and_results(sentence)
trainer.append(features, results)
print("trainer %s appended. Start to train" % predictor)
trainer.train(
os.path.join(os.path.dirname(os.path.abspath(__file__)), '..',
'..', 'model', 'gikry', "crf_%s.model" % predictor))
def train_all_models(filename):
sentences = loadUDGrammasFromFile(filename)
predictors = [
'pos',
'mood',
'voice',
'nameType',
'poss',
'reflex',
'degree',
'number',
'case',
'gender',
'verbForm',
]
for predictor in predictors:
trainer = Trainer(verbose=True)
features_builder = FeaturesBuilder(predictor)
for sentence in sentences:
(features,
results) = features_builder.make_features_and_results(sentence)
trainer.append(features, results)
print("trainer %s appended. Start to train" % predictor)
trainer.train(
os.path.join(os.path.dirname(os.path.abspath(__file__)), '..',
'..', 'model', 'ud', 'crf_%s.model' % predictor))
def train_all_models(filename):
# считываем строки из файла в формате OC и переводим в граммы
sentences = loadOCGrammasFromFile(filename)
# создаем тренера и загружаем грамемы (предложения) в него
predictors = [
'pos',
'gender',
'animacy',
'number',
'case',
'aspect',
'mood',
'person',
'poss',
'reflex',
'tense',
'verbForm',
'voice',
'degree',
'nameType',
'trans',
'invl',
'additional' # Дополнительные теги учим по одному
]
for predictor in predictors:
print("start to train %s" % predictor)
trainer = Trainer(verbose=True)
features_builder = FeaturesBuilder(predictor)
for sencence in sentences:
(features, results) = features_builder.make_features_and_results(sencence)
trainer.append(features, results)
print("trainer %s appended. Start to train" % predictor)
trainer.train(
os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'model', 'oc', "crf_%s.model" % predictor)
)
# отдельные теги быстро учатся, но плохо и долго работают. не учим отдельно
# additionalTags = [
# 'Infr',
# 'Slng',
# 'Arch',
# 'Litr',
# 'Erro',
# 'Dist',
# 'Ques',
# 'Dmns',
# 'Prnt',
# 'V-be',
# 'V-en',
# 'V-ie',
# 'V-bi',
# 'V-ey',
# 'V-oy',
# 'Coun',
# 'Af-p',
# 'Anph',
# 'Subx',
# 'Vpre',
# 'Prdx',
# 'Coll',
# 'Adjx',
# 'Qual',
# 'Apro',
# 'Anum',
# 'Poss',
# 'ms-f',
# 'Ms-f',
# 'Impe',
# 'Impx',
# 'Mult',
# 'Abbr',
# 'Fixd',
# ]
# for additionalTag in additionalTags:
# print("start to train additional tag %s" % additionalTag)
# trainer = Trainer()
# features_builder = FeaturesBuilder('additional', additionalTag)
# for sentence in sentences:
# (features, results) = features_builder.make_features_and_results(sentence)
# trainer.append(features, results)
# print("trainer additiona - %s appended. Start to train" % additionalTag)
# trainer.train(
# os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'model', 'oc', "crf_additional_%s.model" % additionalTag)
# )