Transformerbasert

Transformerbasert refers to systems or methods that utilize the transformer architecture, a type of neural network that has become highly influential in natural language processing and increasingly in other domains like computer vision. The core innovation of the transformer architecture is its self-attention mechanism. This mechanism allows the model to weigh the importance of different words or parts of an input sequence when processing it, regardless of their position. This is a departure from earlier recurrent neural network (RNN) models, which processed information sequentially and struggled with long-range dependencies.

The transformer architecture was first introduced in the paper "Attention Is All You Need" in 2017. Its parallelizable nature and ability to capture complex relationships within data have led to its widespread adoption. Large language models (LLMs) such as GPT-3, BERT, and T5 are prominent examples of transformer-based systems. These models are trained on vast amounts of text data and can perform a wide range of natural language tasks, including text generation, translation, question answering, and summarization. The effectiveness of transformerbasert models has driven significant advancements in artificial intelligence research and applications.