https://github.com/dev-geof/final-state-transformer
Machine learning development toolkit built upon Transformer encoder network architectures and tailored for the realm of high-energy physics and particle-collision event analysis.
https://github.com/dev-geof/final-state-transformer
deep-learning machine-learning multi-head-attention particle-physics science-research toolkit transformer
Last synced: 5 months ago
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Machine learning development toolkit built upon Transformer encoder network architectures and tailored for the realm of high-energy physics and particle-collision event analysis.
- Host: GitHub
- URL: https://github.com/dev-geof/final-state-transformer
- Owner: dev-geof
- License: mit
- Created: 2024-03-30T13:43:15.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-07T13:47:21.000Z (over 1 year ago)
- Last Synced: 2025-09-04T21:48:21.619Z (10 months ago)
- Topics: deep-learning, machine-learning, multi-head-attention, particle-physics, science-research, toolkit, transformer
- Language: Python
- Homepage:
- Size: 4.9 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# FINAL STATE TRANSFORMER
Introducing 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 integration of Transformer networks into research workflows, enabling scientists and researchers to harness state-of-the-art machine learning techniques.
[Documentation]( https://dev-geof.github.io/final-state-transformer/)