docstr: Create dataset objects for splits of the SNLI dataset.
  
  This is the most flexible way to use the dataset.
  
  Args:
      text_field: The field that will be used for premise and hypothesis
          data.
      label_field: The field that will be used for label data.
      parse_field: The field that will be used for shift-reduce parser
          transitions, or None to not include them.
      extra_fields: A dict[json_key: Tuple(field_name, Field)]
      root: The root directory that the dataset's zip archive will be
          expanded into.
      train: The filename of the train data. Default: 'train.jsonl'.
      validation: The filename of the validation data, or None to not
          load the validation set. Default: 'dev.jsonl'.
      test: The filename of the test data, or None to not load the test
          set. Default: 'test.jsonl'.
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code: def splits(
      cls,
      text_field,
      label_field,
      parse_field=None,
      extra_fields=None,
      root=".data",
      train="train.jsonl",
      validation="val.jsonl",
      test="test.jsonl",
  ):
      """Create dataset objects for splits of the SNLI dataset.
  
      This is the most flexible way to use the dataset.
  
      Args:
          text_field: The field that will be used for premise and hypothesis
              data.
          label_field: The field that will be used for label data.
          parse_field: The field that will be used for shift-reduce parser
              transitions, or None to not include them.
          extra_fields: A dict[json_key: Tuple(field_name, Field)]
          root: The root directory that the dataset's zip archive will be
              expanded into.
          train: The filename of the train data. Default: 'train.jsonl'.
          validation: The filename of the validation data, or None to not
              load the validation set. Default: 'dev.jsonl'.
          test: The filename of the test data, or None to not load the test
              set. Default: 'test.jsonl'.
      """
      if extra_fields is None:
          extra_fields = {}
      path = cls.download(root)
  
      if parse_field is None:
          fields = {
              "sentence1": ("premise", text_field),
              "sentence2": ("hypothesis", text_field),
              "gold_label": ("label", label_field),
          }
      else:
          fields = {
              "sentence1_binary_parse": [
                  ("premise", text_field),
                  ("premise_transitions", parse_field),
              ],
              "sentence2_binary_parse": [
                  ("hypothesis", text_field),
                  ("hypothesis_transitions", parse_field),
              ],
              "gold_label": ("label", label_field),
          }
  
      for key in extra_fields:
          if key not in fields.keys():
              fields[key] = extra_fields[key]
  
      return super(NLIDataset, cls).splits(
          path,
          root,
          train,
          validation,
          test,
          format="json",
          fields=fields,
          filter_pred=lambda ex: ex.label != "-",
      )
  
imports: from ..None import data
label_desc: Use image node_id 2 for calling the NLIDataset obj's underlying member method code with example usage: obj.splits(cls, text_field, label_field, parse_field, extra_fields, root, train, validation, test) and returns: super