class_docstr: Processor for entailment data sets used to train NLI models for zero shot intent classification.
class_docstr_tok: ["Processor","for","entailment","data","sets","used","to","train","NLI","models","for","zero","shot","intent","classification","."]
docstr: Creates examples for the training and dev sets.
docstr_tok: ["Creates","examples","for","the","training","and","dev","sets","."]
code: def _create_examples(self, lines: List[str], set_type: str):
      """Creates examples for the training and dev sets."""
      examples = []
      for i, line in enumerate(lines):
          if i == 0:
              continue
          guid = "%s-%s" % (set_type, line[0])
          text_a = line[self.sent1_col]
          text_b = line[self.sent2_col]
          label = line[self.label_col]
          if label == "-":
              continue
          examples.append(
              InputExample(
                  guid=guid, text_a=text_a, text_b=text_b, label=label
              )
          )
      return examples
  
imports: import os
  import pickle
  from typing import Dict, List, Optional
  import torch
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.data_utils.data_preprocessing import DataProcessor, fill_class_weights, get_freq_weights, get_label_stats
  from nemo.collections.nlp.data.glue_benchmark.data_processors import InputExample
  from nemo.collections.nlp.data.glue_benchmark.glue_benchmark_dataset import GLUEDataset
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.neural_types import CategoricalValuesType, ChannelType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the ZeroShotIntentProcessor obj's underlying member method code with example usage: obj._create_examples(lines, set_type) and returns: examples