vanilla Transformer

Vanilla Transformer是一种基于Transformer模型的架构，它是在原始Transformer模型的基础上进行了精简和修改。Vanilla Transformer主要使用了原Transformer中的decode部分结构，包括带有mask的attention层和ff层。相比于原Transformer，Vanilla Transformer的网络深度更深，这导致训练时很难收敛。因此，为了达到收敛的目的，作者采用了一些小的trick，例如使用辅助Loss等。这些trick对于解决类似问题非常有帮助。

相关问题

vanilla transformer

Vanilla Transformer是一种基于Transformer架构的模型。与原始的Transformer相比，Vanilla Transformer只使用了Transformer中解码器部分的结构，即带有mask的attention层和前馈神经网络层。它在网络深度上做了一些改进，导致在训练过程中更难收敛。

Vanilla Transformer的训练过程中，作者采用了一些小trick来帮助模型更好地收敛。其中一种trick是使用了三种辅助Loss，这些辅助Loss在训练过程中起到了正则化的作用。

the vanilla transformer

The vanilla Transformer is a deep learning model that has been widely used in various fields, such as natural language processing (NLP), computer vision (CV), and speech processing. It was originally proposed as a sequence-to-sequence model for machine translation. The core module of the vanilla Transformer is the attention mechanism, which allows the model to focus on different parts of the input sequence when generating the output sequence.

There have been many variants of the vanilla Transformer proposed, including modifications to the architecture, pre-training methods, and applications. These variants have achieved state-of-the-art performance on various tasks and have become the go-to architecture in NLP, especially for pre-trained models. The vanilla Transformer has also been adopted in other disciplines, such as CV, audio processing, chemistry, and life sciences.