The Transformer Architecture

Author(s): Derrick Mwiti

Originally published on Towards AI.

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In 2017, Ashish Vaswani et al wrote a paper that would change the natural language processing (NLP) scene forever, and most recently even computer vision. The authors proposed an efficient way to solve NLP problems without using Recurrent or Convolutional Neural Networks.

The architecture they proposed– the Transformer– would later be used to build state-of-the-art language models that have since taken over the world. This architecture is the precursor of the current wave of generative language models that have now become the new world assistants. To understand how we got here, we have to back to 2017 where it all started, and look at the Transformer architecture in detail.

In this dive, we review the Transformer architecture to make it easier to understand language models based on it.

The Transformer was a game changer because it didn’t require recurrence or convolutions. These were replaced by attention, making training faster through parallelization. The transformer eschews recurrent networks, using a pure attention mechanism instead.

The Transformer is made up of stacked self-attention and fully-connected layers in the decoder and encoder. In the following sections, we discuss the building blocks of… Read the full blog for free on Medium.

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Published via Towards AI