bge-m3-ko-h100

H100 fine-tuned Korean embedding model based on dragonkue/BGE-m3-ko.

This is the better fit for the local AutoRAG corpus among the two uploaded models. Compared with qwen3-embedding-h100, it is the stronger model to highlight for the domain-specific benchmark.

Training

• Platform: H100 Slurm
• Model: dragonkue/BGE-m3-ko
• Finetune run: 111677_20260506_114341_both_2gpu

Benchmark Results

AutoRAG

• Corpus size: 720
• Queries evaluated: 114
• MRR: 0.7773
• MAP: 0.7773
• Hit@1: 0.6754
• Hit@5: 0.9123
• Hit@10: 0.9474
• Hit@50: 0.9825

MIRACL

• Task: MIRACLRetrieval
• Main score: 0.50
• Dataset revision: 9c09abc13478308c27598f350e31d8f06b9b5481

cutoff	Precision	Recall	F1	mAP	mRR	NDCG
@1	0.39906	0.25093	0.30812	0.25093	0.399061	0.39906
@3	0.26604	0.43788	0.33099	0.34631	0.50313	0.43568
@5	0.20282	0.52623	0.29279	0.37510	0.523787	0.45984
@10	0.13380	0.64972	0.22190	0.40375	0.537201	0.50233
@20	0.08052	0.72989	0.14504	0.41724	0.540598	0.53096
@100	0.02272	0.90287	0.04432	0.43030	0.543259	0.57932
@1000	0.00254	0.98881	0.00507	0.43149	0.543447	0.59457

Artifacts

• Model: output/111677_20260506_114341_both_2gpu/bge
• AutoRAG benchmark: benchmark_results/autorag_benchmark.json
• MIRACL summary: benchmark_results/miracl_benchmark.txt
• MIRACL details: benchmark_results/mteb/MIRACLRetrieval.json

Comparison note

• Strongest AutoRAG result of the two uploaded models
• Weaker MIRACL-KO result than rtfin-qwen3-embedding-h100
• Below dragonkue/snowflake-arctic-embed-l-v2.0-ko and rtfin-qwen3-embedding-h100 on the public Korean retrieval leaderboard, but still a strong local-domain model
• Good to cite when the audience cares about the local domain corpus rather than the widest public Korean retrieval leaderboard