code: def get_matthews_corr(data, predictions):
      # only cola is using this...?
      new_predictions = []
      for prediction in predictions:
          if prediction.strip() == "acceptable":
              new_predictions.append(1.0)
          else:
              new_predictions.append(0.0)
      new_gold = []
      for dp in data:
          if dp[0] == "acceptable":
              new_gold.append(1.0)
          else:
              new_gold.append(0.0)
      return matthews_corrcoef(new_gold, new_predictions)
  
file_path: OpenPrompt/openprompt/utils/crossfit_metrics.py
imports: import numpy
  import string
  import re
  from collections import Counter
  from sklearn.metrics import matthews_corrcoef, f1_score
  from scipy.stats import pearsonr, spearmanr
  from rouge import Rouge
label_desc: Use image node_id 2 for calling a global function with example usage: get_matthews_corr(data, predictions) and returns: matthews_corrcoef