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 = {}
      word_order = self.metric.count("+")
      if self.chrf_type == "m-popovic":
          from .utils.chrf import computeChrF
  
          score = computeChrF(
              reference_corpus.text,
              generate_corpus.text,
              nworder=word_order,
          )
          results[self.metric] = score * 100
  
      elif self.chrf_type == "sacrebleu":
          from sacrebleu import corpus_chrf
  
          score = corpus_chrf(
              generate_corpus.text,
              reference_corpus.text,
              word_order=word_order,
          )
          results[self.metric] = score.score
      return results
  
imports: from .abstract_evaluator import AbstractEvaluator
label_desc: Use image node_id 2 for calling the ChrfEvaluator obj's underlying member method code with example usage: obj._calc_metrics_info(generate_corpus, reference_corpus) and returns: results