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: Args:
      sent1_col: the index of the column containing the premise (or sentence 1)
      sent2_col: the index of the column containing the hypothesis (or sentence 2)
      label_col: the index of the column containing the label
      num_classes: number of classes in the data (should be either 2 or 3, corresponding to
      labels ['entailment', 'not_entailment'] or ["contradiction", "entailment", "neutral"])
docstr_tok: ["Args",":","sent1_col",":","the","index","of","the","column","containing","the","premise","(","or","sentence","1",")","sent2_col",":","the","index","of","the","column","containing","the","hypothesis","(","or","sentence","2",")","label_col",":","the","index","of","the","column","containing","the","label","num_classes",":","number","of","classes","in","the","data","(","should","be","either","2","or","3",",","corresponding","to","labels","[","'entailment","'",",","'not_entailment","'","]","or","[","``","contradiction","''",",","``","entailment","''",",","``","neutral","''","]",")"]
code: def __init__(
      self,
      sent1_col: int,
      sent2_col: int,
      label_col: int,
      num_classes: int,
  ):
      """
      Args:
          sent1_col: the index of the column containing the premise (or sentence 1)
          sent2_col: the index of the column containing the hypothesis (or sentence 2)
          label_col: the index of the column containing the label
          num_classes: number of classes in the data (should be either 2 or 3, corresponding to
          labels ['entailment', 'not_entailment'] or ["contradiction", "entailment", "neutral"])
      """
      self.sent1_col = sent1_col
      self.sent2_col = sent2_col
      self.label_col = label_col
      self.num_classes = num_classes
  
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 1 to create a new ZeroShotIntentProcessor object from inherited base classes: DataProcessor with example: obj = ZeroShotIntentProcessor(sent1_col, sent2_col, label_col, num_classes)