class_docstr: Processor for the HANS data set.
docstr: See base class.
  Note that we follow the standard three labels for MNLI
  (see :class:`~transformers.data.processors.utils.MnliProcessor`)
  but the HANS evaluation groups `contradiction` and `neutral` into `non-entailment` (label 0) while
  `entailment` is label 1.
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code: def get_labels(self):
      """See base class.
      Note that we follow the standard three labels for MNLI
      (see :class:`~transformers.data.processors.utils.MnliProcessor`)
      but the HANS evaluation groups `contradiction` and `neutral` into `non-entailment` (label 0) while
      `entailment` is label 1."""
      return ["contradiction", "entailment", "neutral"]
  
file_path: transformers/examples/research_projects/adversarial/utils_hans.py
imports: import logging
  import os
  from dataclasses import dataclass
  from typing import List, Optional, Union
  import tqdm
  from filelock import FileLock
  from transformers import BartTokenizer, BartTokenizerFast, DataProcessor, PreTrainedTokenizer, RobertaTokenizer, RobertaTokenizerFast, XLMRobertaTokenizer, is_tf_available, is_torch_available
label_desc: Use image node_id 3 for calling the HansProcessor obj's underlying member method code with example usage: obj.get_labels() and returns: list