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<h1><a href="https://nn.labml.ai/transformers/fnet/index.html">FNet：将令牌与傅里叶变换混合</a></h1>
<p>这是论文《<a href="https://arxiv.org/abs/2105.03824">FNet：将代币与傅里叶变换混合》的 PyTor</a> <a href="https://pytorch.org">ch</a> 实现。</p>
<p>本文用两个<a href="https://en.wikipedia.org/wiki/Discrete_Fourier_transform">傅里叶变</a>换取代了<a href="https://nn.labml.ai/transformers//mha.html">自我注意力层</a>，以<em>混合</em>令牌。这比自我注意力高7倍。在 GLUE <a href="https://paperswithcode.com/dataset/glue">基准测试</a>中，<a href="https://paperswithcode.com/method/bert">BERT</a> 使用它而不是自我注意力的准确性损失约为92％。</p>
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