def train_intent_classifier(intents): """ Train intent classifier model :param intents: :return: """ X = [] y = [] for intent in intents: training_data = intent.trainingData for example in training_data: if example.get("text").strip() == "": continue X.append(example.get("text")) y.append(str(intent.id)) intent_classifier = EmbeddingIntentClassifier() intent_classifier.train(X,y)
def train_intent_classifier(intents): """ Train intent classifier model :param intents: :return: """ X = [] y = [] for intent in intents: training_data = intent.trainingData for example in training_data: if example.get("text").strip() == "": continue X.append(example.get("text")) y.append(str(intent.intentId)) intent_classifier = EmbeddingIntentClassifier() intent_classifier.train(X,y) intent_classifier.persist(model_dir=app.config["MODELS_DIR"])
def update_model(app, message, **extra): """ Signal hook to be called after training is completed. Reloads ml models and synonyms. :param app: :param message: :param extra: :return: """ global sentence_classifier sentence_classifier = EmbeddingIntentClassifier.load(app.config["MODELS_DIR"]) synonyms = get_synonyms() global entity_extraction entity_extraction = EntityExtractor(synonyms) app.logger.info("Intent Model updated")
def update_model(app, message, **extra): """ Signal hook to be called after training is completed. Reloads ml models and synonyms. :param app: :param message: :param extra: :return: """ global sentence_classifier sentence_classifier = EmbeddingIntentClassifier.load( app.config["MODELS_DIR"]) synonyms = get_synonyms() global entity_extraction entity_extraction = EntityExtractor(synonyms) app.logger.info("Intent Model updated")
def train_intent_classifier(intents): """ Train intent classifier model :param intents: :return: """ X = [] y = [] for intent in intents: training_data = intent.trainingData for example in training_data: if example.get("text").strip() == "": continue X.append(example.get("text")) y.append(str(intent.intentId.encode('utf8'))) intent_classifier = EmbeddingIntentClassifier(use_word_vectors=app.config['USE_WORD_VECTORS']) intent_classifier.train(X, y) intent_classifier.persist(model_dir=app.config["MODELS_DIR"])