class_docstr: Bert Score Evaluator. Now, we support metrics `'bert score'`.
      
code: def _calc_metrics_info(self, generate_corpus, reference_corpus):
      transformers.tokenization_utils.logger.setLevel(logging.ERROR)
      transformers.configuration_utils.logger.setLevel(logging.ERROR)
      transformers.modeling_utils.logger.setLevel(logging.ERROR)
  
      results = {}
      _, _, f_score = score(
          generate_corpus.text,
          reference_corpus.text,
          lang=self.lang,
          batch_size=self.batch_size,
          device=self.device,
      )
      results["bertscore"] = f_score.mean().item() * 100
      return results
  
imports: import logging
  import transformers
  from bert_score import score
  from .abstract_evaluator import AbstractEvaluator
label_desc: Use image node_id 2 for calling the BertScoreEvaluator obj's underlying member method code with example usage: obj._calc_metrics_info(generate_corpus, reference_corpus) and returns: results