code: def _score_for_model(meta):
      
      mean_acc = list()
      pipes = meta["pipeline"]
      acc = meta["accuracy"]
      if "tagger" in pipes:
          mean_acc.append(acc["tags_acc"])
      if "parser" in pipes:
          mean_acc.append((acc["uas"] + acc["las"]) / 2)
      if "ner" in pipes:
          mean_acc.append((acc["ents_p"] + acc["ents_r"] + acc["ents_f"]) / 3)
      return sum(mean_acc) / len(mean_acc)
docs: Returns mean score between tasks in pipeline that can be used for early stopping.