class_docstr: :class:`ConllLoader` 支持读取的数据格式：以空行隔开两个 sample，除了分割行之外的每一行用空格或者制表符隔开不同的元素。如下例所示::
  
      # 文件中的内容
      Nadim NNP B-NP B-PER
      Ladki NNP I-NP I-PER
  
      AL-AIN NNP B-NP B-LOC
      United NNP B-NP B-LOC
      Arab NNP I-NP I-LOC
      Emirates NNPS I-NP I-LOC
      1996-12-06 CD I-NP O
      ...
  
      # 如果用以下的参数读取，返回的DataSet将包含raw_words和pos两个field, 这两个field的值分别取自于第0列与第1列
      dataset = ConllLoader(headers=['raw_words', 'pos'], indexes=[0, 1])._load('/path/to/train.conll')
      # 如果用以下的参数读取，返回的DataSet将包含raw_words和ner两个field, 这两个field的值分别取自于第0列与第2列
      dataset = ConllLoader(headers=['raw_words', 'ner'], indexes=[0, 3])._load('/path/to/train.conll')
      # 如果用以下的参数读取，返回的DataSet将包含raw_words, pos和ner三个field
      dataset = ConllLoader(headers=['raw_words', 'pos', 'ner'], indexes=[0, 1, 3])._load('/path/to/train.conll')
  
  :class:`ConllLoader` 返回的 :class:`~fastNLP.core.DataSet` 的 `field` 由传入的 ``headers`` 确定。
  
  :param headers: 每一列数据的名称， ``header`` 与 ``indexes`` 一一对应
  :param sep: 指定分隔符，默认为制表符
  :param indexes: 需要保留的数据列下标，从 **0** 开始。若为 ``None`` ，则所有列都保留。
  :param dropna: 是否忽略非法数据，若为 ``False`` ，则遇到非法数据时抛出 :class:`ValueError` 。
  :param drophashtag: 是否忽略以 ``#`` 开头的句子。
class_docstr_tok: [":","class",":","`","ConllLoader","`","\u652f\u6301\u8bfb\u53d6\u7684\u6570\u636e\u683c\u5f0f\uff1a\u4ee5\u7a7a\u884c\u9694\u5f00\u4e24\u4e2a","sample\uff0c\u9664\u4e86\u5206\u5272\u884c\u4e4b\u5916\u7684\u6bcf\u4e00\u884c\u7528\u7a7a\u683c\u6216\u8005\u5236\u8868\u7b26\u9694\u5f00\u4e0d\u540c\u7684\u5143\u7d20\u3002\u5982\u4e0b\u4f8b\u6240\u793a",":",":","#","\u6587\u4ef6\u4e2d\u7684\u5185\u5bb9","Nadim","NNP","B-NP","B-PER","Ladki","NNP","I-NP","I-PER","AL-AIN","NNP","B-NP","B-LOC","United","NNP","B-NP","B-LOC","Arab","NNP","I-NP","I-LOC","Emirates","NNPS","I-NP","I-LOC","1996-12-06","CD","I-NP","O","...","#","\u5982\u679c\u7528\u4ee5\u4e0b\u7684\u53c2\u6570\u8bfb\u53d6\uff0c\u8fd4\u56de\u7684DataSet\u5c06\u5305\u542braw_words\u548cpos\u4e24\u4e2afield",",","\u8fd9\u4e24\u4e2afield\u7684\u503c\u5206\u522b\u53d6\u81ea\u4e8e\u7b2c0\u5217\u4e0e\u7b2c1\u5217","dataset","=","ConllLoader","(","headers=","[","'raw_words","'",",","'pos","'","]",",","indexes=","[","0",",","1","]",")","._load","(","'\/path\/to\/train.conll","'",")","#","\u5982\u679c\u7528\u4ee5\u4e0b\u7684\u53c2\u6570\u8bfb\u53d6\uff0c\u8fd4\u56de\u7684DataSet\u5c06\u5305\u542braw_words\u548cner\u4e24\u4e2afield",",","\u8fd9\u4e24\u4e2afield\u7684\u503c\u5206\u522b\u53d6\u81ea\u4e8e\u7b2c0\u5217\u4e0e\u7b2c2\u5217","dataset","=","ConllLoader","(","headers=","[","'raw_words","'",",","'ner","'","]",",","indexes=","[","0",",","3","]",")","._load","(","'\/path\/to\/train.conll","'",")","#","\u5982\u679c\u7528\u4ee5\u4e0b\u7684\u53c2\u6570\u8bfb\u53d6\uff0c\u8fd4\u56de\u7684DataSet\u5c06\u5305\u542braw_words",",","pos\u548cner\u4e09\u4e2afield","dataset","=","ConllLoader","(","headers=","[","'raw_words","'",",","'pos","'",",","'ner","'","]",",","indexes=","[","0",",","1",",","3","]",")","._load","(","'\/path\/to\/train.conll","'",")",":","class",":","`","ConllLoader","`","\u8fd4\u56de\u7684",":","class",":","`","~fastNLP.core.DataSet","`","\u7684","`","field","`","\u7531\u4f20\u5165\u7684","``","headers","``","\u786e\u5b9a\u3002",":","param","headers",":","\u6bcf\u4e00\u5217\u6570\u636e\u7684\u540d\u79f0\uff0c","``","header","``","\u4e0e","``","indexes","``","\u4e00\u4e00\u5bf9\u5e94",":","param","sep",":","\u6307\u5b9a\u5206\u9694\u7b26\uff0c\u9ed8\u8ba4\u4e3a\u5236\u8868\u7b26",":","param","indexes",":","\u9700\u8981\u4fdd\u7559\u7684\u6570\u636e\u5217\u4e0b\u6807\uff0c\u4ece","*","*","0","*","*","\u5f00\u59cb\u3002\u82e5\u4e3a","``","None","``","\uff0c\u5219\u6240\u6709\u5217\u90fd\u4fdd\u7559\u3002",":","param","dropna",":","\u662f\u5426\u5ffd\u7565\u975e\u6cd5\u6570\u636e\uff0c\u82e5\u4e3a","``","False","``","\uff0c\u5219\u9047\u5230\u975e\u6cd5\u6570\u636e\u65f6\u629b\u51fa",":","class",":","`","ValueError","`","\u3002",":","param","drophashtag",":","\u662f\u5426\u5ffd\u7565\u4ee5","``","#","``","\u5f00\u5934\u7684\u53e5\u5b50\u3002"]
code: def __init__(
      self,
      headers: List[str],
      sep: str = None,
      indexes: List[int] = None,
      dropna: bool = True,
      drophash: bool = True,
  ):
      super(ConllLoader, self).__init__()
      if not isinstance(headers, (list, tuple)):
          raise TypeError(
              "invalid headers: {}, should be list of strings".format(
                  headers
              )
          )
      self.headers = headers
      self.dropna = dropna
      self.drophash = drophash
      self.sep = sep
      if indexes is None:
          self.indexes = list(range(len(self.headers)))
      else:
          if len(indexes) != len(headers):
              raise ValueError
          self.indexes = indexes
  
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 to create a new ConllLoader object from inherited base classes: Loader with example: obj = ConllLoader(headers, sep, indexes, dropna, drophash)