What Is a Transformer?

Learn how transformer models function, including their self-attention mechanism, encoder-decoder architecture, and positional encoding. Understand their applications in modern NLP tasks such as spell correction, machine translation, and language modeling to build advanced grammar correction systems.

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Transformer overview

The transformer is a deep learning model architecture introduced in the paper “Attention Is All You Need”Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. “Attention is all you need.” Advances in neural information processing systems 30 (2017).. It revolutionized NLP tasks by replacing traditional recurrent neural networks (RNNs) with a self-attention mechanism, enabling more efficient and parallelizable processing of sequences, in our case word or character sequences. The transformer architecture has been widely adopted and achieved state-of-the-art results not only for spell checking but across the field of machine learning. In fact, some of the most well-known architectures, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT) utilize transformer-based encoder-decoder models. Here is an explanation of the transformer as well as some ML Applications:

1.Introduction

2.Edit Distance

3.Basic Spellchecker

4.Modern Spell Check Methods

5.Part-of-Speech Tagging

6.Basic Grammatical Error Checking

7.Modern Grammar Error Correction Methods

Mini Project

8.Conclusion

Project

What Is a Transformer?

Transformer overview

Self-attention