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K-NRM: End-to-End Neural Ad-hoc Ranking with Kernel Pooling

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Hello! I'd like to know about the details of embedding usage in this model: should the values in the embedding be set to range (0,1); is it ok if i...

K-NRM/knrm/model/model_knrm.py文件下的177行,mu = tf.reshape(input_mu, shape=[1, 1, self.n_bins])有什么特殊意义吗?在104行tmp = tf.exp(-tf.square(tf.sub(rs_sim, mu)) / (tf.mul(tf.square(sigma), 2))), 使用tf.sub这个api计算的时候,我测试用不用reshape结果是一样的。

你好,阅读过你们的paper,很赞,可以提供paper中使用的搜狗训练数据吗?