JaColBERTv2.5

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},
}