change selfcheck to task.py

lm_eval/tasks/selfcheckgpt/task.py

@@ -100,8 +100,8 @@ def process_results(self, doc, results):
  passage = response_temperature_0,
  sampled_passages = other_responses,
  )
- return {"avg": selfcheckgpt_scores["doc_level"]["avg_neg_logprob"],
- "max": selfcheckgpt_scores["doc_level"]["avg_max_neg_logprob"]}
+ return {'avg-selfcheckgpt': selfcheckgpt_scores['doc_level']['avg_neg_logprob'],
+ 'max-selfcheckgpt': selfcheckgpt_scores['doc_level']['avg_max_neg_logprob']}
 
  elif self.selfcheckgpt_type == 'SelfCheckBERTScore':
  selfcheckgpt_scores = self.selfcheckgpt.predict(
@@ -126,20 +126,20 @@ def process_results(self, doc, results):
  selfcheckgpt_scores_avg = sum(selfcheckgpt_scores) / len(selfcheckgpt_scores) if len(selfcheckgpt_scores) > 0 else 0
  selfcheckgpt_scores_max = max(selfcheckgpt_scores)
 
- return {'avg': selfcheckgpt_scores_avg, 'max': selfcheckgpt_scores_max}
+ return {'avg-selfcheckgpt': selfcheckgpt_scores_avg, 'max-selfcheckgpt': selfcheckgpt_scores_max}
 
  def aggregation(self):
  """
  :returns: {str: [float] -> float}
  A dictionary where keys are the names of submetrics and values are
  functions that aggregate a list of metrics
  """
- return {k: mean for k in ["avg", "max"]}
+ return {k: mean for k in ["avg-selfcheckgpt", "max-selfcheckgpt"]}
 
  def higher_is_better(self):
  """
  :returns: {str: bool}
  A dictionary where keys are the names of submetrics and values are
  whether a higher value of the submetric is better
  """
- return {k: False for k in ["avg", "max"]}
+ return {k: False for k in ["avg-selfcheckgpt", "max-selfcheckgpt"]}