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: Gets the list of labels for this data set.
docstr_tok: ["Gets","the","list","of","labels","for","this","data","set","."]
code: def get_labels(self):
      """Gets the list of labels for this data set."""
      if self.num_classes == 2:
          return ["not_entailment", "entailment"]
      elif self.num_classes == 3:
          return ["contradiction", "entailment", "neutral"]
      else:
          raise ValueError("num_classes must be either 2 or 3!")
  
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 4 for calling the ZeroShotIntentProcessor obj's underlying member method code with example usage: obj.get_labels() and returns: list, list