code: def evaluate(predictions, data, metric, **kwargs):
      def cast_to_float(predictions):
          new_predictions = []
          for prediction in predictions:
              try:
                  new_predictions.append(float(prediction.strip()))
              except:
                  new_predictions.append(float("NaN"))
          assert len(new_predictions) == len(predictions)
          return new_predictions
  
      assert len(predictions) == len(data)
  
      if metric == "EM":
          ems = []
          for prediction, dp in zip(predictions, data):
              ems.append(get_exact_match_over_list(prediction, dp))
          return np.mean(ems)
      elif metric == "ACC":
          accs = []
          for prediction, dp in zip(predictions, data):
              accs.append(
                  get_accruacy_over_list(prediction, dp, **kwargs)
              )
          return np.mean(accs)
      elif metric == "QA-F1":  # haven't be tested
          f1s = []
          for prediction, dp in zip(predictions, data):
              f1s.append(get_f1_over_list(prediction, dp))
          return np.mean(f1s)
      elif metric == "Classification-F1":
          return f1_score(
              [dp for dp in data], predictions, average="macro"
          )
      elif metric == "Matthew-Correlation":  # haven't be tested
          return get_matthews_corr(data, predictions)
      elif metric == "Pearson-Correlation":  # haven't be tested
          predictions = cast_to_float(predictions)
          return pearsonr([float(dp[0]) for dp in data], predictions)[0]
      elif metric == "Rouge-L":  # haven't be tested
          rouges = []
          for prediction, dp in zip(predictions, data):
              rouges.append(get_rouge_over_list(prediction, dp))
          return np.mean(rouges)
  
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 1 for calling a global function with example usage: evaluate(predictions, data, metric) and returns: new_predictions, np, np, np, f1_score, get_matthews_corr, pearsonr, np