Model reference · open weights
JaColBERT is an open-weight embedding model from answerdotai. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Maker | answerdotai |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 111M |
| Context | 512 tokens |
| Runs with | RAGatouille |
| Based on | cl-tohoku/bert-base-japanese-v3, bclavie/JaColBERT |
| Released | 2024-07-25 |
| Popularity | 2k downloads / month |
| Licence | Open weights |
About
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},
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys jacolbert for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (jacolbert below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"jacolbert","input":"text to embed"}'
Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.