72c304fa7cac16ed19d8bc75a017f17c8073dd2f,parlai/core/torch_generator_agent.py,TorchGeneratorAgent,_compute_fairseq_bleu,#TorchGeneratorAgent#Any#Any#,691

Before Change

            return 0
        aa = torch.IntTensor(1)
        for i, t in enumerate(texts):
            self.fairseq_bleu_scorer.add(
                batch.label_vec[i].type_as(aa),
                self._vectorize_text(t, True, True, self.label_truncate, False).type_as(
                    aa
                ),
            )

    def _compute_nltk_bleu(self, batch: Batch, texts: List[str]):
        
        Compute BLEU score between text and label(s), using the NLTK BLEU Scorer.

After Change

            all_results.append(result)

        bleu_scores = list(zip(*all_results))
        for k in range(4):
            self.record_local_metric(f"fairseq_bleu{k + 1}", bleu_scores[k])

    def _compute_nltk_bleu(self, batch: Batch, texts: List[str]):
        
        Compute BLEU score between text and label(s), using the NLTK BLEU Scorer.

Instances