code: def evaluate_ems(prediction_file, ground_truth_file, dev_num=3000):
      prediction_list = read_prediction(prediction_file)
      ground_truths_list = []
  
      if ground_truth_file.endswith(("txt", "lst")):
          raw_data = open(ground_truth_file, "r")
      else:
          with open(ground_truth_file, "r") as f:
              raw_data = json.load(f)
      if "dev" in ground_truth_file:
          raw_data = raw_data[:dev_num]
          prediction_list = prediction_list[:dev_num]
  
      for each in raw_data:
          if ground_truth_file.endswith("txt"):
              each = json.loads(each)
  
          if "answers" in each:
              ground_truths_list.append(each["answers"])
          elif "answer" in each:
              ground_truths_list.append(each["answer"])
          else:
              ground_truths_list.append([each])
  
      exactmatch = []
  
      good_example_list = []
      for i, each in enumerate(prediction_list):
          score = ems(each, ground_truths_list[i])
          exactmatch.append(score)
          if score:
              good_example_list.append(i)
  
      final_em_score = np.mean(exactmatch)
  
      print("Exact Match: %.4f;" % final_em_score)
  
      print("done :-)")
  
      return final_em_score, exactmatch
  
file_path: megatron-lm/tools/retro/text_generation/evaluate.py
imports: import sys
  import os
  from tqdm import tqdm
  import string
  import json
  import regex
  import numpy
  from tools.retro.text_generation.metrics import F1Metric
label_desc: Use image node_id 7 for calling a global function with example usage: evaluate_ems(prediction_file, ground_truth_file, dev_num) and returns: final_em_score,  exactmatch