Transformers are a type of neural network architecture that has significantly advanced the field of natural language processing (NLP). They were introduced in the influential paper “Attention is All You Need” by Google Research and have since become the state-of-the-art (SOTA) model for various NLP tasks. Unlike traditional models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, transformers utilize an encoder-decoder structure and a unique attention mechanism that allows them to process data more efficiently and effectively.

The architecture of transformers enables them to handle sequential data, making them particularly suitable for tasks like text analytics and language translation. The attention mechanism allows the model to focus on different parts of the input sequence, improving its ability to understand context and relationships within the data. This has led to their widespread adoption in various applications, including chatbots, translation services, and content generation ²³.

If you’re interested in learning more about transformers, their architecture, or their applications, feel free to ask!