class_docstr: Bleu Evaluator. Now, we support metrics `'self-bleu'`.
      
class_docstr_tok: ["Bleu","Evaluator",".","Now",",","we","support","metrics","`","'self-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.n_grams
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.n_grams"]
code: def _calc_metrics_info(self, generate_corpus, reference_corpus=None):
      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.n_grams
      """
  
      results = {}
      for ngram in self.ngrams:
          results[ngram] = []
  
      self_bleu = SelfBLEU(
          generate_corpus.tokens,
          dict(zip(self.ngrams, self.ngram_weights)),
      )
      scores = self_bleu.get_score()
      for ngram in self.ngrams:
          results[ngram] = [s * 100 for s in scores[ngram]]
      return results
  
imports: from fast_bleu import SelfBLEU
  from .abstract_evaluator import AbstractEvaluator
label_desc: Use image node_id 3 for calling the SelfBleuEvaluator obj's underlying member method code with example usage: obj._calc_metrics_info(generate_corpus, reference_corpus) and returns: results