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import os
from BARTScore.bart_score import BARTScorer
def get_scorers():
assert os.path.isfile(
os.path.join("BARTScore", "bart.pth")
), "You must download `bart.pth` to use BARTScore.\nUse `gdown --id 1_7JfF7KOInb7ZrxKHIigTMR4ChVET01m --output bart.pth`"
scorers = {}
scorers["vanilla"] = BARTScorer(device="cuda:0", checkpoint="facebook/bart-large")
scorers["cnn"] = BARTScorer(device="cuda:0", checkpoint="facebook/bart-large-cnn")
# for the parabank model, first init a bart model, then load the local para model from BARTScore/bart.pth
# see the documentation from https://github.com/neulab/BARTScore for reference
scorers["para"] = BARTScorer(device="cuda:0", checkpoint="facebook/bart-large-cnn")
scorers["para"].load(path="BARTScore/bart.pth")
return scorers
def compute_bart_score_for_scorer(predictions, references, scorer_name, scorer):
precisions = scorer.score(predictions, references, batch_size=4)
recalls = scorer.score(references, predictions, batch_size=4)
f_scores = 0.5 * (precisions + recalls)
return [
{
f"{scorer_name}_f_score": f_scores[i],
f"{scorer_name}_precision": precisions[i],
f"{scorer_name}_recall": recalls[i],
}
for i in len(range(predictions))
]
def compute_bart_score(predictions, references, scorers):
result = [{} for _ in len(range(predictions))]
for scorer_name, scorer in scorers.items():
scorer_result = compute_bart_score_for_scorer(predictions, references, scorer_name, scorer)
for i, element in enumerate(scorer_result):
result[i].update(element)
return result
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