code: def evaluate(options):
      MyMetric = Metric
      if options.task == "NER":
          extractor = NERExtractor(
              options.language, options.NAN, options.prefix
          )
      elif options.task == "RE":
          extractor = REExtractor(
              options.language, options.NAN, options.prefix
          )
      elif options.task == "EE":
          extractor = EEExtractor(
              options.language, options.NAN, options.prefix
          )
          MyMetric = EEMetric
      elif options.task == "EET":
          extractor = EETExtractor(
              options.language, options.NAN, options.prefix
          )
      elif options.task == "EEA":
          extractor = EEAExtractor(
              options.language, options.NAN, options.prefix
          )
          MyMetric = EEMetric
      else:
          raise KeyError
  
      error = []
      standard_mapper = {}
      with open(options.standard_path, "r") as f1:
          for line in f1:
              data = json.loads(line.strip())
              kg = extractor.extract(data["input"], data["output"])
              if kg is None:
                  error.append(("gold", data["output"]))
                  kg = []
              kg = low(kg)
              data["kg"] = kg
              standard_mapper[data["input"]] = data
  
      submit_mapper = {}
      with open(options.submit_path, "r") as f1:
          for line in f1:
              data = json.loads(line.strip())
              kg = extractor.extract(data["input"], data["output"])
              if kg is None:
                  error.append(("pred", data["output"]))
                  kg = []
              kg = low(kg)
              data["kg"] = kg
              submit_mapper[data["input"]] = data
  
      metric = MyMetric(
          options.match_mode,
          options.language,
          metrics_list=options.metrics_list,
      )
      miss_list = []
      for key, gold_record in standard_mapper.items():
          try:
              pred_record = submit_mapper[key]
          except KeyError:
              miss_list.append(key)
          else:
              metric.count_instance(
                  gold_list=gold_record["kg"],
                  pred_list=pred_record["kg"],
                  gold_text=gold_record["output"],
                  pred_text=pred_record["output"],
              )
  
      score = metric.compute_score()
      print(score)
  
      print("eeror", error)
      if len(miss_list) != 0:
          print("Miss:", len(miss_list))
  
file_path: DeepKE/example/llm/InstructKGC/kg2instruction/evaluate.py
imports: import sys
  import json
  import re
  import argparse
  from eval.metrics import Metric, EEMetric
  from eval.extracter import NERExtractor, REExtractor, EEExtractor, EETExtractor, EEAExtractor
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate(options) without return types