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按照博客里说的“具体来说，Sentence-BERT是将u,v,|u−v|（其中|u−v|是指u−v的每个元素都取绝对值后构成的向量）拼接起来做为特征，后面接一个全连接层做2分类（如果是NLI数据集则是3分类）。” 为什么在train/supervised.py里output = keras.layers.Dense(5, use_bias=False)(output)
output = keras.layers.Dense(5, use_bias=False)(output)
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按照博客里说的“具体来说，Sentence-BERT是将u,v,|u−v|（其中|u−v|是指u−v的每个元素都取绝对值后构成的向量）拼接起来做为特征，后面接一个全连接层做2分类（如果是NLI数据集则是3分类）。”
为什么在train/supervised.py里
output = keras.layers.Dense(5, use_bias=False)(output)The text was updated successfully, but these errors were encountered: