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import ast
import statistics
import sacrebleu
from rouge_score import rouge_scorer
def sentence_bleu(references, predictions):
scores = []
for reference, prediction in zip(references, predictions):
scores.append(
sacrebleu.sentence_bleu(
prediction,
reference if isinstance(reference, list) else [reference],
smooth_method="exp",
smooth_value=0.0,
lowercase=True,
tokenize="intl",
).score
)
return statistics.mean(scores)
def corpus_bleu(references, predictions):
return sacrebleu.corpus_bleu(
predictions,
references,
smooth_method="exp",
smooth_value=0.0,
lowercase=True,
tokenize="intl",
).score
def rouge(scope, references, predictions):
rouge = f"rouge{scope}"
scorer = rouge_scorer.RougeScorer([rouge], use_stemmer=True)
references = [r if isinstance(r,list) else [r] for r in references]
scores_dict = {"precision": 0, "recall": 0, "f1": 0}
for refs, prediction in zip(references, predictions):
best_f1 = 0
for ref in refs:
scores = scorer.score(ref, prediction)
if scores[rouge].fmeasure > best_f1:
best_f1 = scores[rouge].fmeasure
scores_dict["precision"] += scores[rouge].precision
scores_dict["recall"] += scores[rouge].recall
scores_dict["f1"] += scores[rouge].fmeasure
return {k: v / len(references) for k, v in scores_dict.items()}
def parse_string(string):
try:
return list(ast.literal_eval(string))
except (ValueError, SyntaxError) as e:
print(f"Error parsing string: {e}")
return None
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