class_docstr: Compute metrics to evaluate social bias.
  
  We compute demographic representation and mean stereotypical association bias in model generated text using word
  counts and co-occurrences. Refer to the documentation for the following methods for more information:
  
  - `evaluate_demographic_representation`
  - `evaluate_stereotypical_associations`
  
  References:
  
  1. Garg et al. 2018      | https://arxiv.org/abs/1711.08412
  2. Bolukbasi et al. 2016 | https://arxiv.org/abs/1607.06520
class_docstr_tok: ["Compute","metrics","to","evaluate","social","bias",".","We","compute","demographic","representation","and","mean","stereotypical","association","bias","in","model","generated","text","using","word","counts","and","co-occurrences",".","Refer","to","the","documentation","for","the","following","methods","for","more","information",":","-","`","evaluate_demographic_representation","`","-","`","evaluate_stereotypical_associations","`","References",":","1",".","Garg","et","al",".","2018","|","https",":","\/\/arxiv.org\/abs\/1711.08412","2",".","Bolukbasi","et","al",".","2016","|","https",":","\/\/arxiv.org\/abs\/1607.06520"]
docstr: Return metric name
docstr_tok: ["Return","metric","name"]
code: def get_metric_name(self) -> str:
      """Return metric name"""
      metric_name = f"bias_metric:mode={self.mode},demographic_category={self.demographic_category}"
      if self.target_category:
          metric_name += f",target_category={self.target_category}"
      return metric_name
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/metrics/bias_metrics.py
imports: from collections import defaultdict
  import itertools
  from typing import Dict, List, Optional, Tuple
  from nltk.tokenize import word_tokenize
  import numpy
  from helm.common.request import RequestResult, Sequence
  from helm.benchmark.adaptation.request_state import RequestState
  from .statistic import Stat
  from .metric import Metric
  from .metric_name import MetricName
  from .bias_word_lists import GENDER_TO_WORD_LISTS, RACE_TO_NAME_LISTS, ADJECTIVE_LIST, PROFESSION_LIST
label_desc: Use image node_id 3 for calling the BiasMetric obj's underlying member method code with example usage: obj.get_metric_name() and returns: metric_name