---
inference: false
datasets:
- answerdotai/MMARCO-japanese-32-scored-triplets
- unicamp-dl/mmarco
language:
- ja
pipeline_tag: sentence-similarity
tags:
- ColBERT
- multi-vector
- sentence-transformers
base_model:
- cl-tohoku/bert-base-japanese-v3
- bclavie/JaColBERT
license: mit
library_name: RAGatouille
---

Model weights for the JaColBERTv2.4 checkpoint, which is the pre-post-training version of JaColBERTv2.5, 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.

## Sentence Transformers

As of [Sentence Transformers](https://www.sbert.net/) v6.0.0, this model can also be loaded directly as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:

```bash
pip install "sentence-transformers>=6.0.0" fugashi unidic-lite
```

```python
from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("answerdotai/JaColBERTv2.4")

query = "日本で一番高い山は何ですか？"
documents = [
    "富士山は日本で最も高い山で、標高は3776メートルです。",
    "東京は日本の首都で、世界最大の都市圏の一つです。",
    "北岳は南アルプスにある山で、日本で二番目に高い山です。",
    "富士山は静岡県と山梨県にまたがる活火山です。",
]

query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings[0].shape)
# torch.Size([32, 128]) torch.Size([21, 128])

# MaxSim late-interaction scoring (higher is more relevant)
scores = model.similarity(query_embeddings, document_embeddings)
print(scores)
# tensor([[30.6411, 28.8692, 29.7601, 28.9441]], device='cuda:0')
```

```
@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}, 
}
```