Comments (6)
xjtuleeyf
commented on May 28, 2024
您好，我想补充一下我的问题，我现在好像发现了问题所在，self.hierarchical_class好像是对题目中的标签的个数统计，也就是说，比如我的知识结构中的一级标签是A1,A2,A3等，但是他们从未以一个单独的标签在knowledge中出现过，都是以A1-A12-A123的形式出现的，所以相当于模型默认了我只有二级标签和三级标签，是这个意思吗？如果是这样的话有什么解决方案呢，期待您的回复，多谢
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MagiaSN
commented on May 28, 2024
可以把一、二级标签也补充到数据中，例如一条数据标签为["A1-A12-A123"]，将父类目也扩充到标签中，变为["A1", "A1-A2", "A1-A12-A123"]
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xjtuleeyf
commented on May 28, 2024
明白了，感谢解答，麻烦还有一个小问题想请教您，在修改global2local的参数之后，代码不再报之前的错误了，但是在loss.py和train.py中的有个参数logits，总是报错这个参数应该是一个tensor，实际上是一个长度为3的tuple，为了让代码正常运行，我在loss.py和train.py中的报错位置加入了logits = logits[0]，经过运行发现效果(F1 = 0.50)远差于利用TextRNN的结果（F1 = 0.74），而且在用TextRNN运行模型的时候不会报这种错误，期待得到您的解答！
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xjtuleeyf
commented on May 28, 2024
另外还想请问一下存储标签层次的taxonomy文件在哪个部分发挥作用呢？
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MagiaSN
commented on May 28, 2024
另外还想请问一下存储标签层次的taxonomy文件在哪个部分发挥作用呢？
taxonomy文件中包含的标签父子关系会用于loss中的hierarchical penalty计算等
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Related Issues (20)
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