class_docstr: Reads in the Stanford NLI dataset and the MultiGenre NLI dataset
docstr: data_splits specified which data split to use (train, dev, test).
  Expects that self.dataset_folder contains the files s1.$data_split.gz,  s2.$data_split.gz,
  labels.$data_split.gz, e.g., for the train split, s1.train.gz, s2.train.gz, labels.train.gz
code: def get_examples(self, filename, max_examples=0):
      """
      data_splits specified which data split to use (train, dev, test).
      Expects that self.dataset_folder contains the files s1.$data_split.gz,  s2.$data_split.gz,
      labels.$data_split.gz, e.g., for the train split, s1.train.gz, s2.train.gz, labels.train.gz
      """
      s1 = gzip.open(
          os.path.join(self.dataset_folder, "s1." + filename),
          mode="rt",
          encoding="utf-8",
      ).readlines()
      s2 = gzip.open(
          os.path.join(self.dataset_folder, "s2." + filename),
          mode="rt",
          encoding="utf-8",
      ).readlines()
      labels = gzip.open(
          os.path.join(self.dataset_folder, "labels." + filename),
          mode="rt",
          encoding="utf-8",
      ).readlines()
  
      examples = []
      id = 0
      for sentence_a, sentence_b, label in zip(s1, s2, labels):
          guid = "%s-%d" % (filename, id)
          id += 1
          examples.append(
              InputExample(
                  guid=guid,
                  texts=[sentence_a, sentence_b],
                  label=self.map_label(label),
              )
          )
  
          if 0 < max_examples <= len(examples):
              break
  
      return examples
  
file_path: sentence-transformers/sentence_transformers/readers/NLIDataReader.py
imports: from .None import InputExample
  import gzip
  import os
label_desc: Use image node_id 2 for calling the NLIDataReader obj's underlying member method code with example usage: obj.get_examples(filename, max_examples) and returns: examples