demo

text_similarity_scorer.py

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+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+class TextSimilarityScorer:
+    def __init__(self, model_name='bert-base-chinese'):
+        # 初始化 BERT 模型，这里使用中文预训练模型
+        self.model = SentenceTransformer(model_name)
+        
+    def calculate_similarity(self, standard_answer, student_answer):
+        # 获取文本嵌入
+        standard_embedding = self.model.encode([standard_answer])
+        student_embedding = self.model.encode([student_answer])
+        
+        # 计算余弦相似度
+        similarity = cosine_similarity(standard_embedding, student_embedding)[0][0]
+        return similarity
+    
+    def score(self, standard_answer, student_answer, max_score=100):
+        # 计算相似度
+        similarity = self.calculate_similarity(standard_answer, student_answer)
+        
+        # 将相似度转换为分数
+        score = similarity * max_score
+        
+        # 四舍五入到整数
+        return round(score)
+
+# 使用示例
+def main():
+    # 初始化评分器
+    scorer = TextSimilarityScorer()
+    
+    # 示例标准答案和学生答案
+    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。"
+    student_answers = [
+        "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
+        "哈哈哈哈哈哈。",  # 比较相似
+        "人工智能是计算机科学的重要领域。"  # 不太相关
+    ]
+    
+    # 对每个学生答案进行评分
+    for i, student_answer in enumerate(student_answers, 1):
+        similarity = scorer.calculate_similarity(standard_answer, student_answer)
+        score = scorer.score(standard_answer, student_answer)
+        print(f"\n学生答案 {i}:")
+        print(f"答案: {student_answer}")
+        print(f"相似度: {similarity:.2f}")
+        print(f"得分: {score}")
+
+if __name__ == "__main__":
+    main()