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LeeSureman avatar LeeSureman commented on August 22, 2024 2

这里是只用到了两个特征，主要是出于在效果等价的情况下节省显存的考虑，因为我们现在用的是一层的transformer嘛，过了transformer之后得到的词的表示我们是不需要的，只需要用字的表示去做标签分类，由于字的长度始终是1，所以ss和ee就足以携带字和词之间的位置关系了。但是对于两层及以上的transformer，ss和ee不足以建模词与词之间的位置关系，这个时候就需要用到4个相对位置特征了

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nefujiangping avatar nefujiangping commented on August 22, 2024

好的，明白啦，谢谢你的回复～

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LawsonAbs avatar LawsonAbs commented on August 22, 2024

后得到的词的表示我们是不需要的，只需要用字的表示去做标签分类，由于字的长度始终是1，所以ss和ee就足以携带字和词之间的位置关系了。但是对于两层及以

为什么“过了transformer之后得到的词的表示我们是不需要的”？？？ 是因为词的embedding都是从字典中找到训练好的表示吗？

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LeeSureman avatar LeeSureman commented on August 22, 2024

后得到的词的表示我们是不需要的，只需要用字的表示去做标签分类，由于字的长度始终是1，所以ss和ee就足以携带字和词之间的位置关系了。但是对于两层及以

为什么“过了transformer之后得到的词的表示我们是不需要的”？？？ 是因为词的embedding都是从字典中找到训练好的表示吗？

我们最终只需要在字token上做序列标注

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