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 doc_to_answering_text(self, doc):
      has_answer = doc["ctxs"]["has_answer"]
      answering_index = has_answer.index(True)
      answering_contexts = {
          k: v[answering_index : answering_index + 1]
          for k, v in doc["ctxs"].items()
      }
      answer_candidate = (
          "[題名]:"
          + answering_contexts["title"][0]
          + self.SEP
          + "[問題]:"
          + answering_contexts["text"][0]
      )
      qa_prompt = self.doc_to_qa_prompt(doc)
      return answer_candidate + self.SEP + qa_prompt
  
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 10 for calling the JAQKETV2 obj's underlying member method code with example usage: obj.doc_to_answering_text(doc) and returns: unknown