class_docstr: A class named Evaluator includes GPT-3.5/GPT-4 evaluation
docstr: A comprehensive evaluation of the answers from the model.
  The function evaluates the model's performance from different perspectives
  using GPT-3.5, GPT-4, and off-the-shelf evaluation metrics.
  
  The metrics will be decided by the config file.
code: def evaluate(
      self,
      answers: List[Dict],
      targets: List[Dict],
      save_path: str,
      model_name: str,
  ) -> None:
      """
      A comprehensive evaluation of the answers from the model.
      The function evaluates the model's performance from different perspectives
      using GPT-3.5, GPT-4, and off-the-shelf evaluation metrics.
  
      The metrics will be decided by the config file.
  
      """
  
      answers_per_category = get_data_per_category(
          answers, list(self.params.keys())
      )
      targets_per_category = get_data_per_category(
          targets, list(self.params.keys())
      )
  
      # gpt evaluation
      for category in self.params:
          if len(answers_per_category[category]) == 0:
              print(
                  f"Category {category} specified in your config doesn't have corresponding answers!"
              )
              continue
  
          if self.params[category].get("GPT", None) is None:
              continue
  
          category_metrics = self.params[category]["GPT"]
  
          prompt = self.gpt_evaluation_prompt.get(category, None)
          if prompt is None:
              print(
                  f"No prompt for category {category}! Use prompt for category general now."
              )
              prompt = self.gpt_evaluation_prompt["general"]
  
          self.gpt_evaluation_results[category] = gpt_evaluate.evaluate(
              answers_per_category[category],
              prompt,
              category_metrics,
              category,
              save_path,
              model_name,
              self.gpt_model,
              self.language,
              references=targets_per_category[category]
              if self.gpt_with_reference
              else None,
          )
  
file_path: ColossalAI/applications/ColossalEval/colossal_eval/evaluate/evaluator.py
imports: import os
  from typing import Any, Dict, List
  import colossal_eval.evaluate.gpt_evaluate
  from .utils import get_data_per_category
label_desc: Use image node_id 3 for calling the Evaluator obj's underlying member method code with example usage: obj.evaluate(answers, targets, save_path, model_name) without return types