class_docstr: Processor for the XNLI data set.
class_docstr_tok: ["Processor","for","the","XNLI","data","set","."]
docstr: See base class.
docstr_tok: ["See","base","class","."]
code: def get_train_examples(self, data_dir):
      """See base class."""
      lines = self._read_tsv(
          os.path.join(
              data_dir,
              "multinli",
              "multinli.train.%s.tsv" % self.language,
          )
      )
      examples = []
      for i, line in enumerate(lines):
          if i == 0:
              continue
          guid = "train-%d" % (i)
          text_a = tokenization.convert_to_unicode(line[0])
          text_b = tokenization.convert_to_unicode(line[1])
          label = tokenization.convert_to_unicode(line[2])
          if label == tokenization.convert_to_unicode("contradictory"):
              label = tokenization.convert_to_unicode("contradiction")
          examples.append(
              InputExample(
                  guid=guid, text_a=text_a, text_b=text_b, label=label
              )
          )
      return examples
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import csv
  import os
  import mesh_tensorflow
  import mesh_tensorflow.bert.bert
  import mesh_tensorflow.bert.optimization
  import mesh_tensorflow.bert.tokenization
  from six.moves import range
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 2 for calling the XnliProcessor obj's underlying member method code with example usage: obj.get_train_examples(data_dir) and returns: examples