Transformeripõhiste

Transformeripõhiste refers to methods and systems that are based on the transformer architecture, a type of neural network. This architecture, introduced in the 2017 paper "Attention Is All You Need," revolutionized natural language processing and has since found applications in many other fields, including computer vision and audio processing.

The core innovation of the transformer architecture is the self-attention mechanism. This mechanism allows the model to weigh the importance of different parts of the input sequence when processing each element. Unlike previous recurrent neural networks (RNNs) and convolutional neural networks (CNNs) that processed data sequentially or with fixed receptive fields, self-attention enables transformers to capture long-range dependencies in data much more effectively. This is achieved by calculating attention scores between all pairs of input elements, allowing the model to focus on relevant information regardless of its position.

Transformeripõhiste models have achieved state-of-the-art results in a wide array of tasks. In natural language processing, they power large language models (LLMs) like GPT and BERT, which excel at text generation, translation, summarization, and question answering. In computer vision, variants of the transformer architecture, such as Vision Transformers (ViTs), have demonstrated competitive performance on image classification and object detection. The adaptability and scalability of the transformer architecture have made it a dominant force in modern artificial intelligence research and development.