Transformers Boost AI: 90% Accuracy in Name Generation

The Transformer architecture has significantly impacted AI, achieving high performance in various applications like image recognition and robotics. It’s particularly noted for its use in models like ChatGPT. Understanding Transformers can be approached by considering simple tasks like generating random names character by character. A basic model predicts the next character based on the frequency of characters following the previous one in a dataset of common names. This article delves into the Transformer’s complexity, focusing on its core component, the Attention mechanism. This mechanism uses cosine similarity to understand relationships between tokens in a sequence, enhancing the model’s accuracy to around 90% in name generation tasks. The article also covers the implementation of query, key, and value in the Attention architecture, providing a practical guide to building and understanding these state-of-the-art models.

Source: towardsdatascience.com