class_docstr: Processor for the DuEE data set.
class_docstr_tok: ["Processor","for","the","DuEE","data","set","."]
docstr: Creates examples for the training and dev sets.
docstr_tok: ["Creates","examples","for","the","training","and","dev","sets","."]
code: def _create_examples(self, file_path, set_type):
      """Creates examples for the training and dev sets."""
  
      examples = []
  
      for idx, line in enumerate(open(file_path).readlines()[1:]):
          e_id = "%s-%s" % (set_type, idx)
  
          datas = line.strip("\n").split("\t")
          words = datas[0].split()
          labels = datas[1].split()
          trigger_word_ids = (
              None if self.task_name == "trigger" else datas[2].split()
          )
  
          examples.append(
              InputExample(
                  guid=e_id,
                  words=words,
                  labels=labels,
                  trigger_word_ids=trigger_word_ids,
              )
          )
      return examples
  
imports: from __future__ import absolute_import, division, print_function
  import json
  import logging
  import os
  import sys
  from copy import deepcopy
  from io import open
  from transformers import XLMRobertaTokenizer, BertTokenizer, RobertaTokenizer, AutoTokenizer
  from .utils_ee import read_by_lines, write_by_lines, load_dict, label_data, process_remained_pred_trigger, clear_wrong_tokens
  from torch.nn.utils.rnn import pad_sequence
  from tqdm import tqdm
label_desc: Use image node_id 6 for calling the ACEProcessor obj's underlying member method code with example usage: obj._create_examples(file_path, set_type) and returns: examples