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from .base_metric import SummMetric
from summ_eval.metric import Metric as SEMetric
from typing import List, Dict
class SummEvalMetric(SummMetric):
"""
Generic class for a summarization metric whose backend is SummEval.
"""
def __init__(self, se_metric: SEMetric):
self.se_metric = se_metric
def evaluate(
self, inputs: List[str], targets: List[str], keys: List[str]
) -> Dict[str, float]:
score_dict = self.se_metric.evaluate_batch(inputs, targets)
return {key: score_dict[key] if key in score_dict else None for key in keys}
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