class_docstr: **SNLI** 数据集的 **Loader**，如果您的文章使用了这份数据，请引用
  
      http://nlp.stanford.edu/pubs/snli_paper.pdf
  
  文件每一行是一个 sample，每一行都为一个 ``json`` 对象，其数据格式为::
  
      {"annotator_labels": ["neutral", "entailment", "neutral", "neutral", "neutral"], "captionID": "4705552913.jpg#2",
       "gold_label": "neutral", "pairID": "4705552913.jpg#2r1n",
       "sentence1": "Two women are embracing while holding to go packages.",
       "sentence1_binary_parse": "( ( Two women ) ( ( are ( embracing ( while ( holding ( to ( go packages ) ) ) ) ) ) . ) )",
       "sentence1_parse": "(ROOT (S (NP (CD Two) (NNS women)) (VP (VBP are) (VP (VBG embracing) (SBAR (IN while) (S (NP (VBG holding)) (VP (TO to) (VP (VB go) (NP (NNS packages)))))))) (. .)))",
       "sentence2": "The sisters are hugging goodbye while holding to go packages after just eating lunch.",
       "sentence2_binary_parse": "( ( The sisters ) ( ( are ( ( hugging goodbye ) ( while ( holding ( to ( ( go packages ) ( after ( just ( eating lunch ) ) ) ) ) ) ) ) ) . ) )",
       "sentence2_parse": "(ROOT (S (NP (DT The) (NNS sisters)) (VP (VBP are) (VP (VBG hugging) (NP (UH goodbye)) (PP (IN while) (S (VP (VBG holding) (S (VP (TO to) (VP (VB go) (NP (NNS packages)) (PP (IN after) (S (ADVP (RB just)) (VP (VBG eating) (NP (NN lunch))))))))))))) (. .)))"
       }
  
  读取的 :class:`~fastNLP.core.DataSet` 将具备以下的数据结构：
  
  .. csv-table:: 下面是使用SNLILoader加载的DataSet所具备的field
     :header: "target", "raw_words1", "raw_words2",
  
     "neutral ", "Two women are embracing while holding..", "The sisters are hugging goodbye..."
     "entailment", "Two women are embracing while holding...", "Two woman are holding packages."
     "...", "...", "..."
docstr: 从指定一个或多个路径中的文件中读取数据，返回 :class:`~fastNLP.io.DataBundle` 。
  读取的 field 根据 :class:`SNLILoader` 初始化时传入的 ``fields`` 决定。
  
  :param str paths: 传入一个目录, 将在该目录下寻找 ``snli_1.0_train.jsonl``, ``snli_1.0_dev.jsonl``
      和 ``snli_1.0_test.jsonl`` 三个文件。
  :return:
docstr_tok: ["\u4ece\u6307\u5b9a\u4e00\u4e2a\u6216\u591a\u4e2a\u8def\u5f84\u4e2d\u7684\u6587\u4ef6\u4e2d\u8bfb\u53d6\u6570\u636e\uff0c\u8fd4\u56de",":","class",":","`","~fastNLP.io.DataBundle","`","\u3002","\u8bfb\u53d6\u7684","field","\u6839\u636e",":","class",":","`","SNLILoader","`","\u521d\u59cb\u5316\u65f6\u4f20\u5165\u7684","``","fields","``","\u51b3\u5b9a\u3002",":","param","str","paths",":","\u4f20\u5165\u4e00\u4e2a\u76ee\u5f55",",","\u5c06\u5728\u8be5\u76ee\u5f55\u4e0b\u5bfb\u627e","``","snli_1.0_train.jsonl","``",",","``","snli_1.0_dev.jsonl","``","\u548c","``","snli_1.0_test.jsonl","``","\u4e09\u4e2a\u6587\u4ef6\u3002",":","return",":"]
code: def load(
      self, paths: Union[str, Dict[str, str]] = None
  ) -> DataBundle:
      r"""
      从指定一个或多个路径中的文件中读取数据，返回 :class:`~fastNLP.io.DataBundle` 。
      读取的 field 根据 :class:`SNLILoader` 初始化时传入的 ``fields`` 决定。
  
      :param str paths: 传入一个目录, 将在该目录下寻找 ``snli_1.0_train.jsonl``, ``snli_1.0_dev.jsonl``
          和 ``snli_1.0_test.jsonl`` 三个文件。
      :return:
      """
      _paths = {}
      if paths is None:
          paths = self.download()
      if paths:
          if os.path.isdir(paths):
              if not os.path.isfile(
                  os.path.join(paths, "snli_1.0_train.jsonl")
              ):
                  raise FileNotFoundError(
                      f"snli_1.0_train.jsonl is not found in {paths}"
                  )
              _paths["train"] = os.path.join(
                  paths, "snli_1.0_train.jsonl"
              )
              for filename in [
                  "snli_1.0_dev.jsonl",
                  "snli_1.0_test.jsonl",
              ]:
                  filepath = os.path.join(paths, filename)
                  _paths[
                      filename.split("_")[-1].split(".")[0]
                  ] = filepath
              paths = _paths
          else:
              raise NotADirectoryError(
                  f"{paths} is not a valid directory."
              )
  
      datasets = {
          name: self._load(path) for name, path in paths.items()
      }
      data_bundle = DataBundle(datasets=datasets)
      return data_bundle
  
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 SNLILoader obj's underlying member method code with example usage: obj.load(paths) and returns: data_bundle