class_docstr: **Quora matching** 任务的数据集 **Loader**。
  支持读取的文件中的内容应该有以下的形式：以制表符分隔，且前三列一定分别为 **label** ， **句子1** ， **句子2** 。
  
  Example::
  
      1       How do I get funding for my web based startup idea ?    How do I get seed funding pre product ? 327970
      0       Is honey a viable alternative to sugar for diabetics ?  How would you compare the United States ' euthanasia laws to Denmark ?  90348
      ...
  
  读取的 :class:`~fastNLP.core.DataSet` 将具备以下的数据结构：
  
  .. csv-table::
      :header: "raw_words1", "raw_words2", "target"
  
      "How do I get funding for my web based...", "How do I get seed funding...","1"
      "Is honey a viable alternative ...", "How would you compare the United...","0"
      "...","...","..."
code: def _load(self, path: str):
      ds = DataSet()
  
      with open(path, "r", encoding="utf-8") as f:
          for line in f:
              line = line.strip()
              if line:
                  parts = line.split("\t")
                  raw_words1 = parts[1]
                  raw_words2 = parts[2]
                  target = parts[0]
                  if raw_words1 and raw_words2 and target:
                      ds.append(
                          Instance(
                              raw_words1=raw_words1,
                              raw_words2=raw_words2,
                              target=target,
                          )
                      )
      return ds
  
imports: import os
  from typing import Union, Dict
  from .csv import CSVLoader
  from .json import JsonLoader
  from .loader import Loader
  from fastNLP.io.data_bundle import DataBundle
  from ..utils import check_loader_paths
  from fastNLP.core.dataset import DataSet, Instance
  from fastNLP.core.log import logger
label_desc: Use image node_id 2 for calling the QuoraLoader obj's underlying member method code with example usage: obj._load(path) and returns: ds