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# Copied from https://github.com/huggingface/datasets/blob/76bb45964df1e62d1411b0a9e9fc673e9a791c9a/metrics/sacrebleu/sacrebleu.py
from copy import deepcopy
from sacrebleu.metrics import BLEU
def compute_bleu(
predictions,
references,
smooth_method="exp",
smooth_value=None,
force=False,
lowercase=False,
tokenize=None,
effective_order=False,
):
references_per_prediction = len(references[0])
if any(len(refs) != references_per_prediction for refs in references):
references = deepcopy(references)
max_references_per_prediction = max(len(refs) for refs in references)
for refs in references:
refs.extend([None] * (max_references_per_prediction - len(refs)))
transformed_references = [[refs[i] for refs in references] for i in range(references_per_prediction)]
bleu = BLEU(
smooth_method=smooth_method,
smooth_value=smooth_value,
force=force,
lowercase=lowercase,
effective_order=effective_order,
**(dict(tokenize=tokenize) if tokenize else {}),
)
output = bleu.corpus_score(
predictions,
transformed_references,
)
output_dict = {
"score": output.score,
**{f"counts-{i+1}": round(p, 4) for i, p in enumerate(output.counts)},
**{f"totals-{i+1}": round(p, 4) for i, p in enumerate(output.totals)},
**{f"precision-{i+1}": round(p, 4) for i, p in enumerate(output.precisions)},
"bp": output.bp,
"sys_len": output.sys_len,
"ref_len": output.ref_len,
}
return output_dict
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