def predict(self,model_name, sentence):
"""
Predict NER labels for given model and query
:param model_name:
:param sentence:
:return:
"""
from app.nlu.tasks import pos_tagger
tokenized_sentence = word_tokenize(sentence)
tagged_token = pos_tagger(sentence)
tagger = pycrfsuite.Tagger()
tagger.open("{}/{}.model".format(app.config["MODELS_DIR"], model_name))
predicted_labels = tagger.tag(self.sent_to_features(tagged_token))
extracted_entities = self.crf2json(
zip(tokenized_sentence, predicted_labels))
return self.replace_synonyms(extracted_entities)
def predict(self, model_name, sentence):
"""
Predict NER labels for given model and query
:param model_name:
:param sentence:
:return:
"""
from app.nlu.tasks import pos_tagger
tokenized_sentence = word_tokenize(sentence)
tagged_token = pos_tagger(sentence)
tagger = pycrfsuite.Tagger()
tagger.open("{}/{}.model".format(app.config["MODELS_DIR"], model_name))
predicted_labels = tagger.tag(self.sent_to_features(tagged_token))
extracted_entities = self.crf2json(
zip(tokenized_sentence, predicted_labels))
return extracted_entities
def predict(self, model_name, sentence):
"""
Predict NER labels for given model and query
:param model_name:
:param sentence:
:return:
"""
from app.nlu.tasks import pos_tagger
doc = spacy_tokenizer(sentence)
words = [token.text for token in doc]
tagged_token = pos_tagger(sentence)
tagger = pycrfsuite.Tagger()
tagger.open("{}/{}.model".format(app.config["MODELS_DIR"], model_name))
predicted_labels = tagger.tag(self.sent_to_features(tagged_token))
extracted_entities = self.crf2json(zip(words, predicted_labels))
return self.replace_synonyms(extracted_entities)