def _stats(self, loss, scores, target):
        """
        Args:
            loss (:obj:`FloatTensor`): the loss computed by the loss criterion.
            scores (:obj:`FloatTensor`): a score for each possible output
            target (:obj:`FloatTensor`): true targets

        Returns:
            :obj:`onmt.utils.Statistics` : statistics for this batch.
        """
        pred = scores.max(1)[1]
        non_padding = target.ne(self.padding_idx)
        num_correct = pred.eq(target).masked_select(non_padding).sum().item()
        num_non_padding = non_padding.sum().item()
        # return Statistics(loss.item(), num_non_padding, num_correct)
        return loss.item()

        Args:
            loss (:obj:`FloatTensor`): the loss computed by the loss criterion.
            scores (:obj:`FloatTensor`): a score for each possible output
            target (:obj:`FloatTensor`): true targets

        Returns:
            :obj:`onmt.utils.Statistics` : statistics for this batch.
        
utils_nlp/models/transformers/bertsum/loss.py