nn.embedding层的作用

回答: nn.Embedding层的作用是将离散的输入（如单词或索引）映射到连续的向量空间中。这个层接受两个参数，分别是词汇表大小和嵌入向量的大小。在训练过程中，Embedding层学习将每个离散输入映射到一个连续的嵌入向量，这些嵌入向量可以捕捉到输入之间的语义关系。通过使用Embedding层，我们可以将文本或其它离散型数据转换为连续的向量表示，方便后续的模型处理和学习。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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