Comments (4)
前后窗口指的是训练语句长度,您的这个模型长度设置的应该是70。
from information-extraction-chinese.
多种实体关系,我建议是可以把A_B, A_C, B_C三种实体的feature分别输入,即把一句话变成三句仅仅是实体feature不同其它一样的句子来尝试。
模型精度,可以使用test_GRU.py中的main_for_evaluation
函数来测试。
前后窗口长度,具体指的是?
from information-extraction-chinese.
它这个用的相对于实体的位置,应该没用用到你所说的前后窗口,在训练word2vec中,用到的前后窗口的长度。
from information-extraction-chinese.
我的应用环境就是一句话包含多对实体,句子长度达到512,预测效果还行,但是部署成服务,发现效果不是很好,我也没有找到原因。
from information-extraction-chinese.
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