5ad7a33a04d8829ad3439b5f9390bd136105f986,allennlp/training/metrics/bleu.py,BLEU,_get_modified_precision_counts,#BLEU#Any#Any#Any#,77

Before Change

        
        clipped_matches = 0
        total_predicted = 0
        for batch_num in range(predicted_tokens.size(0)):
            predicted_row = predicted_tokens[batch_num, :]
            reference_row = reference_tokens[batch_num, :]
            predicted_ngram_counts = self._ngrams(predicted_row, ngram_size)
            reference_ngram_counts = self._ngrams(reference_row, ngram_size)
            for ngram, count in predicted_ngram_counts.items():
                clipped_matches += min(count, reference_ngram_counts[ngram])
                total_predicted += count
        return clipped_matches, total_predicted

    def _get_brevity_penalty(self) -> float:
        if self._prediction_lengths > self._reference_lengths:

After Change

        total_predicted = 0
        from allennlp.training.util import ngrams

        for predicted_row, reference_row in zip(predicted_tokens, reference_tokens):
            predicted_ngram_counts = ngrams(predicted_row, ngram_size, self._exclude_indices)
            reference_ngram_counts = ngrams(reference_row, ngram_size, self._exclude_indices)
            for ngram, count in predicted_ngram_counts.items():
                clipped_matches += min(count, reference_ngram_counts[ngram])
                total_predicted += count
        return clipped_matches, total_predicted

    def _get_brevity_penalty(self) -> float:
        if self._prediction_lengths > self._reference_lengths:

Instances