class_docstr: Bleu Evaluator. Now, we support metrics `'bleu'`
      
class_docstr_tok: ["Bleu","Evaluator",".","Now",",","we","support","metrics","`","'bleu","'","`"]
docstr: get metrics result
  
  Args:
      generate_corpus: the generated corpus
      reference_corpus: the referenced corpus
  
  Returns:
      dict: a dict of metrics <metric> which record the results according to self.ngrams
docstr_tok: ["get","metrics","result","Args",":","generate_corpus",":","the","generated","corpus","reference_corpus",":","the","referenced","corpus","Returns",":","dict",":","a","dict","of","metrics","<","metric",">","which","record","the","results","according","to","self.ngrams"]
code: def _calc_metrics_info(self, generate_corpus, reference_corpus):
      r"""get metrics result
  
      Args:
          generate_corpus: the generated corpus
          reference_corpus: the referenced corpus
  
      Returns:
          dict: a dict of metrics <metric> which record the results according to self.ngrams
      """
      results = {}
  
      nist = NISTScore()
      for gen, refs in zip(generate_corpus.text, reference_corpus.text):
          nist.append(gen, refs)
      results["nist"] = nist.score()
      return results
  
imports: from .utils.pymteval import NISTScore
  from .abstract_evaluator import AbstractEvaluator
label_desc: Use image node_id 2 for calling the NistEvaluator obj's underlying member method code with example usage: obj._calc_metrics_info(generate_corpus, reference_corpus) and returns: results