Comments (2)
这两个参数的作用效果有重叠,增大这两个参数值都会增加正类的权重,所以调参的时候考虑固定一个参数,只调整其中一个参数。调重参数可以分析一下p r f1 值,如果p值大而r值小考虑减小正类权重,否则降低权重。另外建议你分析一下词典的特性,是否覆盖面太小导致词典标注的数据没有代表性,如果是的话考虑扩充词典。词典标注是否错误率太高,是的话考虑筛选词典。最后,我们没有在中文数据集上做过实验,所以没有复现一说:)。
from lexiconner.
这两个参数的作用效果有重叠,增大这两个参数值都会增加正类的权重,所以调参的时候考虑固定一个参数,只调整其中一个参数。调重参数可以分析一下p r f1 值,如果p值大而r值小考虑减小正类权重,否则降低权重。另外建议你分析一下词典的特性,是否覆盖面太小导致词典标注的数据没有代表性,如果是的话考虑扩充词典。词典标注是否错误率太高,是的话考虑筛选词典。最后,我们没有在中文数据集上做过实验,所以没有复现一说:)。
嗯嗯,懂了,感谢回答!!
from lexiconner.
Related Issues (14)
- About questions ?
- Training without any supervised labeling
- ontonotes4.0原数据集处理
- Choosing appropriate hyperparameters for the loss function
- AdaPU困惑 HOT 1
- Estimate $\pi_p$
- about the bnPU loss HOT 1
- LOC and MISC type training HOT 4
- Are ``train.XXX.txt'' generated by dictionaries?
- Code issue HOT 1
- AttributeError: 'float' object has no attribute 'backward' HOT 1
- 中文的如何弄? HOT 2
- 关于损失函数
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