2024-02-28 20:14:46 +08:00
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from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
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from rouge import Rouge
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import numpy as np
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import jieba
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def compute_metrics(eval_pred):
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predictions, labels = eval_pred
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# 字符级别
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# decoded_preds = [" ".join((pred.replace(" ", ""))) for pred in predictions]
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# decoded_labels = [" ".join((label.replace(" ", ""))) for label in labels]
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# 词级别
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decoded_preds = [" ".join(jieba.cut(pred.replace(" ", ""))) for pred in predictions]
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decoded_labels = [" ".join(jieba.cut(label.replace(" ", ""))) for label in labels]
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rouge = Rouge()
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2024-03-03 17:20:16 +08:00
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bleu =np.array([0.,0.,0.,0.])
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weights = [(1.,0.,0.,0.),(1./2., 1./2.),(1./3., 1./3., 1./3.),(1./4., 1./4., 1./4., 1./4.)]
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2024-02-28 20:14:46 +08:00
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for decoded_label, decoded_pred in zip(decoded_labels, decoded_preds):
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bleu +=np.array( sentence_bleu(
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references=[decoded_label.split(' ')],
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hypothesis=decoded_pred.split(' '),
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smoothing_function=SmoothingFunction().method1,weights=weights
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))
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bleu /= len(decoded_labels)
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result = rouge.get_scores(decoded_preds, decoded_labels, avg=True)
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result = {key: value['f'] * 100 for key, value in result.items()}
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result["bleu"] = {'bleu_1':bleu[0] * 100,'bleu_2':bleu[1] * 100,'bleu_3':bleu[2] * 100,'bleu_4':bleu[3] * 100}
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return result
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