Transformer

Introduction to the Transformer Model

Welcome to the second post in our NLP series! If you haven’t read the previous post yet, you can find it here. In this post, we will delve into the fascinating world of the Transformer model. The original paper on the Transformer model can be found here. It was published by Vaswani et al. in 2017 and is titled “Attention Is All You Need”. This paper introduced the Transformer architecture and its attention mechanism, which has since become a cornerstone in the field of natural language processing.

The Transformer model has revolutionized the field of natural language processing. Its innovative architecture and powerful features have made it a go-to choice for various NLP tasks, including machine translation, text generation, and sentiment analysis.

Throughout this post, we will explore the inner workings of the Transformer model. We will start by understanding its attention mechanism, which allows the model to focus on relevant parts of the input sequence. We will also discuss the concept of positional encoding, which helps the model understand the order of words in a sentence. Lastly, we will dive into the self-attention layers, which enable the model to capture dependencies between different words.

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