Model weights for the final JaColBERTv2.5 checkpoint, using an entirely overhauled training recipe and trained on just 40% of the data of JaColBERTv2.
This model largely outperforms all previous approaches, including JaColBERTV2 multilingual models such as BGE-M3, on all datasets.
This page will be updated with the full details and the model report in the next few days.
@misc{clavié2024jacolbertv25optimisingmultivectorretrievers,
title={JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resources},
author={Benjamin Clavié},
year={2024},
eprint={2407.20750},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2407.20750},
}
