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FINAL STATE TRANSFORMER

Example of Model Architecture

Final State Transformer (FST) is a machine learning development toolkit built upon Transformer encoder network architectures and specifically crafted for high-energy physics applications. Leveraging the power of the multi-head attention mechanism for capturing long-range dependencies and contextual information in sequences of particle-collision event final-state objects, it allows the design of machine learning models that excel in classification and regression tasks. Featuring a user-friendly interface, this toolkit facilitates the integration of Transformer networks into research workflows, enabling users to harness state-of-the-art machine learning techniques.

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