Transformers can refer to two different things: a type of device used in electrical engineering and a type of model architecture used in natural language processing.
Electrical Transformers: In electrical engineering, a transformer is a device used to transfer electrical energy between two or more circuits through electromagnetic induction. It consists of two or more coils of wire wound around a core, and it can step up or step down the voltage of an alternating current (AC) while keeping the frequency constant.
Transformer Model Architecture: In the context of natural language processing and machine learning, a transformer is a deep learning architecture introduced in the paper "Attention Is All You Need" by Vaswani et al. (2017). It revolutionized the field by using self-attention mechanisms to process sequential data, such as text, and achieved state-of-the-art results on various NLP tasks. The architecture's key innovation is the self-attention mechanism, which allows it to capture relationships between words in a sentence without relying on fixed-length contexts. Transformers have since become the foundation for many advanced NLP models, including BERT, GPT, T5, and more.
It's important to note that the term "transformer" has different meanings depending on the context in which it is used.