#attention mechanism

This neutral guide showcases a broad array of materials for understanding and implementing transformer models, applicable from NLP to computer vision. It features overviews, concise technical insights, tutorials, and applicable examples, suitable for learners and professionals interested in transformers. Highlighted elements include detailed illustrations, technical summaries, and important references such as the 'Attention Is All You Need' paper. The guide also offers practical insights into implementation via resources like the HuggingFace Transformers library.

E(n)-Equivariant Transformer combines Welling's Graph Neural Network with advanced attention mechanisms for tasks such as antibody CDR loop design. It features customizable configurations, atomic and bond embeddings, and the ability to model local and global molecular interactions. The project provides flexible usage and examples for practical applications like protein backbone denoising, ideal for researchers working with molecular data.