code: def load_data_and_labels():
      
      # download dataset
      get_chinese_text()
  
      # Load data from files
      positive_examples = list(codecs.open("./data/pos.txt", "r", "utf-8").readlines())
      positive_examples = [s.strip() for s in positive_examples]
      positive_examples = [pe for pe in positive_examples if len(pe) < 100]
      negative_examples = list(codecs.open("./data/neg.txt", "r", "utf-8").readlines())
      negative_examples = [s.strip() for s in negative_examples]
      negative_examples = [ne for ne in negative_examples if len(ne) < 100]
      # Split by words
      x_text = positive_examples + negative_examples
      # x_text = [clean_str(sent) for sent in x_text]
      x_text = [list(s) for s in x_text]
  
      # Generate labels
      positive_labels = [[0, 1] for _ in positive_examples]
      negative_labels = [[1, 0] for _ in negative_examples]
      y = np.concatenate([positive_labels, negative_labels], 0)
      return [x_text, y]
docs: Loads MR polarity data from files, splits the data into words and generates labels.
      Returns split sentences and labels.