text: from sklearn.preprocessing import MultiLabelBinarizer
  import tfkit.utility.tok as tok
  from tfkit.utility.dataloader import get_multiclas_data_from_file
  
  get_data_from_file = get_multiclas_data_from_file
  
  
  def preprocessing_data(item, tokenizer, maxlen=512, handle_exceed='slide', **kwargs):
      tasks, task, input, target = item
      param_dict = {'input': input, 'tokenizer': tokenizer, 'target': target, 'maxlen': maxlen,
                    'handle_exceed': handle_exceed, 'tasks': tasks, 'task': task}
      yield get_feature_from_data, param_dict
  
  
  def get_feature_from_data(tokenizer, maxlen, tasks, task, input, target=None, handle_exceed='slide',
                            **kwargs):
      feature_dict_list = []
      t_input_list, _ = tok.handle_exceed(tokenizer, input, maxlen - 2, handle_exceed)
      for t_input in t_input_list:  # -2 for cls and sep
          row_dict = dict()
          row_dict['task'] = list(task.encode("utf-8"))
          input_token = [tok.tok_begin(tokenizer)] + t_input + [tok.tok_sep(tokenizer)]
          tokenized_input_id = tokenizer.convert_tokens_to_ids(input_token)
          mask_id = [1] * len(tokenized_input_id)
          tokenized_input_id.extend([tokenizer.pad_token_id] * (maxlen - len(tokenized_input_id)))
          mask_id.extend([-1] * (maxlen - len(mask_id)))
          row_dict['input'] = tokenized_input_id
          row_dict['mask'] = mask_id
          row_dict['target'] = [-1]
          if target is not None:
              if 'multi_label' in task:
                  mlb = MultiLabelBinarizer(classes=tasks[task])
                  tar = mlb.fit_transform([target])
                  tokenize_label = tar
              else:
                  tokenize_label = [tasks[task].index(target[0])]
              row_dict['target'] = tokenize_label
          feature_dict_list.append(row_dict)
      return feature_dict_list
  