52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
|
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()
|