Generative AI - Architecture of the Transformer model

Architecture of the Transformer model

The paper "Attention is All You Need" (2017) by Vaswani et al. presented the Transformer model, which changed the field of natural language processing by fixing the problems that recurrent neural networks (RNNs) and convolutional neural networks (CNNs) had with dealing with sequential data. The most important new thing about the Transformer design is that it uses self-attention methods to process raw data in parallel instead of one at a time. This makes training much more efficient.

An encoder-decoder design makes up the Transformer model:

Encoder: There are several similar layers that make up the encoder. A multi-head self-attention system and a position-wise fully linked feed-forward network are the two sublayers that make up each layer. Each sub-layer gets residual links and layer normalization, which make sure that training is stable and works well.

Decoder: The decoder also has several similar layers, just like the encoder. In addition to the self-attention and feed-forward sub-layers, each layer in the decoder has an extra sub-layer that does multi-head attention over the output of the encoder.

Some important parts of the Transformer model are:

Multiple Head Self-Attention: This feature lets the model focus on different parts of the input stream at the same time, figuring out how they relate to and rely on each other.

Positional Encoding: Because the Transformer doesn't handle the input data in order, positional encodings are added to the input embeddings to tell the computer where each token is in the series.

Feed-Forward Networks: Each layer has fully connected feed-forward networks that process the output of the attention mechanisms. This makes it easier for the model to pick up on complex trends.

The Transformer design is very fast and scalable for many NLP tasks, such as translation, summary, and language modeling, because it can work in parallel and handle long-range relationships well.