Transformer architecture rank #435

Transformer architecture ranks #435 out of leading AI companies with an overall score of 319.7 on SectorHQ's AI Leaderboard as of February 2026. The company has recorded

AI-related events with 0% positive sentiment. Transformer architecture is a deep learning model architecture introduced in the 2017 paper 'Attention Is All You Need.' It relies primarily on a self-attention mechanism to process sequential input data, such as text, and has become the dominant architecture for large language models (LLMs). Unlike previous recurrent neural networks, transformers process entire sequences simultaneously, which allows for more efficient parallelization during training. The architecture consists of an encoder and a decoder, each containing multiple layers of self-attention and feed-forward neural networks. It is the foundational technology for generative pre-trained transformers (GPTs) and other advanced AI systems, enabling significant improvements in machine translation, text generation, and a wide range of natural language processing tasks.