for word_tags in word_tags_list:
        
        bag_of_words = []       
        pattern_words = word_tags[0]
       
        for word in pattern_words:
            index=pattern_words.index(word)
            word=stemmer.stem(word.lower())
            pattern_words[index]=word  

        for word in stem_words:
            if word in pattern_words:
                bag_of_words.append(1)
            else:
                bag_of_words.append(0)
        print(bag_of_words)

        labels_encoding = list(labels) #inicialmente, labels será toda zerada
        tag = word_tags[1] #salve a tag
        tag_index = classes.index(tag)  #vá para o índice da tag
        labels_encoding[tag_index] = 1  #anexe 1 àquele índice
       
        training_data.append([bag_of_words, labels_encoding])

print(training_data[0])

# Crie os dados de treinamento
def preprocess_train_data(training_data):
   
    training_data = np.array(training_data, dtype=object)
    
    train_x = list(training_data[:,0])
    train_y = list(training_data[:,1])

    print(train_x[0])
    print(train_y[0])
  
    return train_x, train_y

train_x, train_y = preprocess_train_data(training_data)