What is Transformer

The term "transformer" can refer to multiple concepts depending on the context. In the field of mathematics and physics, a transformer is a device that changes the voltage of an electrical current, typically used in electrical power transmission and distribution systems.
However, in the context of natural language processing (NLP) and deep learning, a transformer refers to a specific type of neural network architecture called the "Transformer model." The Transformer model was introduced in a seminal research paper titled "Attention Is All You Need" by Vaswani et al. in 2017. It revolutionized the field of NLP and became the foundation for many subsequent advancements.
The Transformer model is designed to process sequential data, such as sentences or words, by leveraging a mechanism called self-attention. Self-attention allows the model to weigh the importance of different parts of the input sequence when generating the output representation. This attention mechanism enables the model to capture dependencies between words or elements in the input sequence more effectively than traditional recurrent neural networks (RNNs).
The Transformer model consists of an encoder and a decoder. The encoder takes an input sequence and processes it to generate a representation, while the decoder takes that representation and generates an output sequence. Transformers have been primarily used for tasks such as machine translation, text summarization, sentiment analysis, and question answering. They have achieved state-of-the-art performance in many NLP benchmarks and have become a fundamental component of modern language models like GPT (Generative Pre-trained Transformer).
Overall, the Transformer model has significantly advanced the field of NLP by providing a powerful and efficient approach to sequence modeling and generation.