52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
from sentence_transformers import SentenceTransformer
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from sklearn.metrics.pairwise import cosine_similarity
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import numpy as np
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class TextSimilarityScorer:
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def __init__(self, model_name='bert-base-chinese'):
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# 初始化 BERT 模型，这里使用中文预训练模型
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self.model = SentenceTransformer(model_name)
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def calculate_similarity(self, standard_answer, student_answer):
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# 获取文本嵌入
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standard_embedding = self.model.encode([standard_answer])
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student_embedding = self.model.encode([student_answer])
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# 计算余弦相似度
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similarity = cosine_similarity(standard_embedding, student_embedding)[0][0]
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return similarity
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def score(self, standard_answer, student_answer, max_score=100):
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# 计算相似度
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similarity = self.calculate_similarity(standard_answer, student_answer)
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# 将相似度转换为分数
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score = similarity * max_score
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# 四舍五入到整数
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return round(score)
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# 使用示例
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def main():
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# 初始化评分器
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scorer = TextSimilarityScorer()
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# 示例标准答案和学生答案
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standard_answer = "机器学习是人工智能的一个子领域，它使用统计学方法让计算机系统能够从数据中学习和改进。"
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student_answers = [
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"机器学习是AI的分支，通过统计方法让计算机从数据中学习。", # 相似但较简短
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"哈哈哈哈哈哈。", # 比较相似
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"人工智能是计算机科学的重要领域。" # 不太相关
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]
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# 对每个学生答案进行评分
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for i, student_answer in enumerate(student_answers, 1):
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similarity = scorer.calculate_similarity(standard_answer, student_answer)
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score = scorer.score(standard_answer, student_answer)
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print(f"\n学生答案 {i}:")
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print(f"答案: {student_answer}")
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print(f"相似度: {similarity:.2f}")
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print(f"得分: {score}")
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if __name__ == "__main__":
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main() |