class_docstr: 支持加载形如以下格式的内容，一行两列，以空格隔开两个 sample
  
  Example::
  
      我 O
      们 O
      变 O
      而 O
      以 O
      书 O
      会 O
      ...
class_docstr_tok: ["\u652f\u6301\u52a0\u8f7d\u5f62\u5982\u4ee5\u4e0b\u683c\u5f0f\u7684\u5185\u5bb9\uff0c\u4e00\u884c\u4e24\u5217\uff0c\u4ee5\u7a7a\u683c\u9694\u5f00\u4e24\u4e2a","sample","Example",":",":","\u6211","O","\u4eec","O","\u53d8","O","\u800c","O","\u4ee5","O","\u4e66","O","\u4f1a","O","..."]
docstr: :param path: 文件路径
  :return: :class:`~fastNLP.core.DataSet` ，包含 ``raw_words`` 列和 ``target`` 列
docstr_tok: [":","param","path",":","\u6587\u4ef6\u8def\u5f84",":","return",":",":","class",":","`","~fastNLP.core.DataSet","`","\uff0c\u5305\u542b","``","raw_words","``","\u5217\u548c","``","target","``","\u5217"]
code: def _load(self, path: str):
      """
      :param path: 文件路径
      :return: :class:`~fastNLP.core.DataSet` ，包含 ``raw_words`` 列和 ``target`` 列
      """
      ds = DataSet()
      with open(path, "r", encoding="utf-8") as f:
          raw_chars = []
          target = []
          for line in f:
              line = line.strip()
              if line:
                  parts = line.split()
                  if len(parts) == 1:  # 网上下载的数据有一些列少tag，默认补充O
                      parts.append("O")
                  raw_chars.append(parts[0])
                  target.append(parts[1])
              else:
                  if raw_chars:
                      ds.append(
                          Instance(raw_chars=raw_chars, target=target)
                      )
                  raw_chars = []
                  target = []
      return ds
  
imports: import glob
  import os
  import random
  import shutil
  import time
  from typing import List
  from .loader import Loader
  from ..file_reader import _read_conll
  from fastNLP.core.dataset import DataSet, Instance
label_desc: Use image node_id 1 for calling the CNNERLoader obj's underlying member method code with example usage: obj._load(path) and returns: ds