class_docstr: prompt template is taken from [日本語に特化した60億パラメータ規模のGPTモデルの構築と評価](https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/H9-4.pdf)
class_docstr_tok: ["prompt","template","is","taken","from","[","\u65e5\u672c\u8a9e\u306b\u7279\u5316\u3057\u305f60\u5104\u30d1\u30e9\u30e1\u30fc\u30bf\u898f\u6a21\u306eGPT\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\u3068\u8a55\u4fa1","]","(","https",":","\/\/www.anlp.jp\/proceedings\/annual_meeting\/2023\/pdf_dir\/H9-4.pdf",")"]
code: def process_results(self, doc, results):
      assert (
          len(results) == 1
      ), f"results should be a list with 1 str element, but is {results}"
      continuation = results[0]
      predictions = {
          "id": doc["id"],
          "prediction_text": continuation,
      }
  
      references = {
          "id": doc["id"],
          "answers": doc["answers"],
      }
      out = {
          "exact_match": (
              predictions,
              references,
          ),  # Exact match (the normalized answer exactly match the gold answer)
          "f1": (
              predictions,
              references,
          ),  # The F-score of predicted tokens versus the gold answer
      }
  
      # add verbose output
      out["details"] = {
          "question": doc["question"],
          "response": continuation,
          "gold": doc["answers"],
      }
  
      return out
  
imports: import os
  import inspect
  import datasets
  from math import exp
  from lm_eval.base import rf, Task
  from functools import partial
  from lm_eval.jasquad import jasquad
label_desc: Use image node_id 12 for calling the JSQuAD obj's underlying member method code with example usage: obj.process_results(doc, results) and returns: out