ReSB2-Base is a domain-adapted version of
jhu-clsp/mmBERT-base, obtained through continued pre-training with the Masked Language Modeling (MLM) objective on the SB2 Dataset.
1@inproceedings{lage2026resb2,
2 title={ReSB²: Machine-Assisted Linking of Legislative Bills using Domain-Adapted ModernBERT and Explainable AI},
3 author={Lage, Lucas Gabriel and others},
4 booktitle={Proceedings of the ACM Conference on Hypertext and Social Media},
5 year={2026}
6}
The models developed and released in this repository, including the trained ReSB² models, are provided for research and academic purposes.
The use of the underlying pre-trained models is subject to the licenses and terms of use defined by their original providers. Users are responsible for complying with the respective licenses when using, modifying, or redistributing these models.
This work was supported by the Legislative Assembly of Minas Gerais (ALMG), CNPq, CAPES, and FAPEMIG.
