From the model card
Fields below are copied from the tags and counters on the HuggingFace repository cl-nagoya/ruri-v3-310m at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- cl-nagoya
- Pipeline tag
- sentence-similarity
- Weight formats
- safetensors
- License tag
apache-2.0— read the license file in the repo before relying on it- Lineage
-
- base model cl-nagoya/ruri-v3-pt-310m
- fine-tune of cl-nagoya/ruri-v3-pt-310m
- Language tags
- Japanese (ja)
- Papers cited
- arXiv:2409.07737
- Datasets declared
- cl-nagoya/ruri-v3-dataset-ft
- Downloads (HF counter at last fetch)
- 479,441
- Likes (HF counter at last fetch)
- 82
- Model card
- https://huggingface.co/cl-nagoya/ruri-v3-310m
Use cases
- Japanese semantic search and document retrieval
- FAQ matching for Japanese customer service systems
- Clustering Japanese text by topic or intent
- Building Japanese RAG retrieval components
- Evaluating ModernBERT effectiveness on Japanese language tasks
Pros
- ModernBERT backbone improves on BERT for long-context Japanese text
- Purpose-built for Japanese; outperforms multilingual embedding models on Japanese retrieval
- 74 likes with Nagoya University academic backing
- 310M provides more capacity than 100M-class Japanese embedding models
Cons
- Japanese only; cannot be used for cross-lingual retrieval
- No published JMTEB or JSQuAD benchmark comparison in the model card
- ModernBERT architecture is relatively new; third-party tooling support may lag
- v3 is a recent release; production stability should be verified with testing
Tags
safetensorsmodernbertsentence-similarityfeature-extractionjadataset:cl-nagoya/ruri-v3-dataset-ftarxiv:2409.07737base_model:cl-nagoya/ruri-v3-pt-310mbase_model:finetune:cl-nagoya/ruri-v3-pt-310mlicense:apache-2.0region:us