Metadata-Version: 2.4
Name: salt-ml
Version: 0.11.0
Summary: Multimodal and Multiclass Machine Learning for High Energy Physics
License: MIT
Project-URL: Homepage, https://gitlab.cern.ch/aft/algorithms/salt
Project-URL: Issue Tracker, https://gitlab.cern.ch/aft/algorithms/salt/-/issues
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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License-File: LICENSE
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Dynamic: license-file

[![docs](https://img.shields.io/badge/info-documentation-informational)](https://ftag-salt.docs.cern.ch/)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.07217/status.svg)](https://doi.org/10.21105/joss.07217)

# Salt

This is the home of Salt, a framework for training multi-model and multi-task models models in the style of GN2.

Documentation is available [here](https://ftag-salt.docs.cern.ch/).

If you use this software, please cite our article in the Journal of Open Source Software.

```bibtex
@article{salt2025,
  author = {Jackson Barr and Diptaparna Biswas and Maxence Draguet and Philipp Gadow and Emil Haines and Osama Karkout and Dmitrii Kobylianskii and Wei Sheng Lai and Matthew Leigh and Nicholas Luongo and Ivan Oleksiyuk and Nikita Pond and Sébastien Rettie and Andrius Vaitkus and Samuel Van Stroud and Johannes Wagner},
  title = {Salt: Multimodal Multitask Machine Learning for High Energy Physics},
  journal = {Journal of Open Source Software},
  volume = {10},
  issue = {112},
  year = {2025},
  doi = {10.21105/joss.07217},
  url = {https://joss.theoj.org/papers/10.21105/joss.07217},
  issn = {2475-9066}
}
```
