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Meet JevEmbed: an open-source framework for embedding-based decisions
Turn embeddings into decisions. Choose, score, and judge with your choice of embedding model.
We’ve open-sourced JevEmbed, a Python framework for three structured decision tasks:
🎯 Choice: select from a set of candidates
📊 Score: rate against ordered criteria
✅ Noul: judge whether a statement or question holds
🔧 JevEmbed currently includes configurations for KaLM, Qwen3, and E5 embedding models. You can use it through a Python API, CLI, or optional HTTP server. It also supports local LoRA fine-tuning, so you can adapt an embedding model to your own decision tasks and load the resulting adapter for local inference.
Fine-tuning results
📈 We trained KaLM-Embedding-V2.5 and Qwen3-Embedding-0.6B on the 79,116-example training split of Open-Jev’s release-v2-redistributable subset. We then evaluated them on 3,495 hard-label questions from the same subset’s held-out validation split.
ZefanCai/Open-Jev
KaLM-Embedding-V2.5: 30.24% base accuracy → 76.68% after LoRA fine-tuning
Qwen3-Embedding-0.6B: 30.73% base accuracy → 84.06% after LoRA fine-tuning
KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5
Qwen/Qwen3-Embedding-0.6B
These results are specific to that validation split. Performance on other tasks and datasets may differ.
JevEmbed also supports Choice tasks with more than 255 candidates, making it useful for classification and routing problems with large candidate sets.
Explore the framework, open an issue, or tell us what decision task you would try it on:
🔗 https://github.com/HITsz-TMG/JevEmbed
#Embeddings #LoRA #SentenceTransformers #OpenSource #JevEmbed
Turn embeddings into decisions. Choose, score, and judge with your choice of embedding model.
We’ve open-sourced JevEmbed, a Python framework for three structured decision tasks:
🎯 Choice: select from a set of candidates
📊 Score: rate against ordered criteria
✅ Noul: judge whether a statement or question holds
🔧 JevEmbed currently includes configurations for KaLM, Qwen3, and E5 embedding models. You can use it through a Python API, CLI, or optional HTTP server. It also supports local LoRA fine-tuning, so you can adapt an embedding model to your own decision tasks and load the resulting adapter for local inference.
Fine-tuning results
📈 We trained KaLM-Embedding-V2.5 and Qwen3-Embedding-0.6B on the 79,116-example training split of Open-Jev’s release-v2-redistributable subset. We then evaluated them on 3,495 hard-label questions from the same subset’s held-out validation split.
ZefanCai/Open-Jev
KaLM-Embedding-V2.5: 30.24% base accuracy → 76.68% after LoRA fine-tuning
Qwen3-Embedding-0.6B: 30.73% base accuracy → 84.06% after LoRA fine-tuning
KaLM-Embedding/KaLM-embedding-multilingual-mini-instruct-v2.5
Qwen/Qwen3-Embedding-0.6B
These results are specific to that validation split. Performance on other tasks and datasets may differ.
JevEmbed also supports Choice tasks with more than 255 candidates, making it useful for classification and routing problems with large candidate sets.
Explore the framework, open an issue, or tell us what decision task you would try it on:
🔗 https://github.com/HITsz-TMG/JevEmbed
#Embeddings #LoRA #SentenceTransformers #OpenSource #JevEmbed
