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.