/AutoRAG-example-korean-embedding-benchmark

AutoRAG example about benchmarking Korean embeddings.

Primary LanguagePython

Korean Embedding Benchmark with AutoRAG

This is a benchmark of Korean embedding models. With AutoRAG, you can make this kind of benchmark easy and fast.

Result

Top-k 1

Model name F1 Recall Precision mAP mRR NDCG
paraphrase-multilingual-mpnet-base-v2 0.3596 0.3596 0.3596 0.3596 0.3596 0.3596
KoSimCSE-roberta 0.4298 0.4298 0.4298 0.4298 0.4298 0.4298
Cohere embed-multilingual-v3.0 0.3596 0.3596 0.3596 0.3596 0.3596 0.3596
openai ada 002 0.4737 0.4737 0.4737 0.4737 0.4737 0.4737
multilingual-e5-large-instruct 0.4649 0.4649 0.4649 0.4649 0.4649 0.4649
Upstage Embedding 0.6579 0.6579 0.6579 0.6579 0.6579 0.6579
paraphrase-multilingual-MiniLM-L12-v2 0.2982 0.2982 0.2982 0.2982 0.2982 0.2982
openai_embed_3_small 0.5439 0.5439 0.5439 0.5439 0.5439 0.5439
ko-sroberta-multitask 0.4211 0.4211 0.4211 0.4211 0.4211 0.4211
openai_embed_3_large 0.6053 0.6053 0.6053 0.6053 0.6053 0.6053
KU-HIAI-ONTHEIT-large-v1 0.7105 0.7105 0.7105 0.7105 0.7105 0.7105
KU-HIAI-ONTHEIT-large-v1.1 0.7193 0.7193 0.7193 0.7193 0.7193 0.7193
kf-deberta-multitask 0.4561 0.4561 0.4561 0.4561 0.4561 0.4561
gte-multilingual-base 0.5877 0.5877 0.5877 0.5877 0.5877 0.5877
bge-m3 0.6754 0.6754 0.6754 0.6754 0.6754 0.6754
KoE5 0.6930 0.6930 0.6930 0.6930 0.6930 0.6930

Top-k 3

Model name F1 Recall Precision mAP mRR NDCG
paraphrase-multilingual-mpnet-base-v2 0.2368 0.4737 0.1579 0.2032 0.2032 0.2712
KoSimCSE-roberta 0.3026 0.6053 0.2018 0.2661 0.2661 0.3515
Cohere embed-multilingual-v3.0 0.2851 0.5702 0.1901 0.2515 0.2515 0.3321
openai ada 002 0.3553 0.7105 0.2368 0.3202 0.3202 0.4186
multilingual-e5-large-instruct 0.3333 0.6667 0.2222 0.2909 0.2909 0.3856
Upstage Embedding 0.4211 0.8421 0.2807 0.3509 0.3509 0.4743
paraphrase-multilingual-MiniLM-L12-v2 0.2061 0.4123 0.1374 0.1740 0.1740 0.2340
openai_embed_3_small 0.3640 0.7281 0.2427 0.3026 0.3026 0.4097
ko-sroberta-multitask 0.2939 0.5877 0.1959 0.2500 0.2500 0.3351
openai_embed_3_large 0.3947 0.7895 0.2632 0.3348 0.3348 0.4491
KU-HIAI-ONTHEIT-large-v1 0.4386 0.8772 0.2924 0.3421 0.3421 0.4766
KU-HIAI-ONTHEIT-large-v1.1 0.4430 0.8860 0.2953 0.3406 0.3406 0.4778
kf-deberta-multitask 0.3158 0.6316 0.2105 0.2792 0.2792 0.3679
gte-multilingual-base 0.4035 0.8070 0.2690 0.3450 0.3450 0.4614
bge-m3 0.4342 0.8684 0.2895 0.3436 0.3436 0.4757
KoE5 0.4386 0.8772 0.2924 0.3406 0.3406 0.4757

Top-k 5

Model name F1 Recall Precision mAP mRR NDCG
paraphrase-multilingual-mpnet-base-v2 0.1813 0.5439 0.1088 0.1575 0.1575 0.2491
KoSimCSE-roberta 0.2164 0.6491 0.1298 0.1751 0.1751 0.2873
Cohere embed-multilingual-v3.0 0.2076 0.6228 0.1246 0.1640 0.1640 0.2731
openai ada 002 0.2602 0.7807 0.1561 0.2139 0.2139 0.3486
multilingual-e5-large-instruct 0.2544 0.7632 0.1526 0.2194 0.2194 0.3487
Upstage Embedding 0.2982 0.8947 0.1789 0.2237 0.2237 0.3822
paraphrase-multilingual-MiniLM-L12-v2 0.1637 0.4912 0.0982 0.1437 0.1437 0.2264
openai_embed_3_small 0.2690 0.8070 0.1614 0.2148 0.2148 0.3553
ko-sroberta-multitask 0.2164 0.6491 0.1298 0.1697 0.1697 0.2835
openai_embed_3_large 0.2807 0.8421 0.1684 0.2088 0.2088 0.3586
KU-HIAI-ONTHEIT-large-v1 0.3041 0.9123 0.1825 0.2137 0.2137 0.3783
KU-HIAI-ONTHEIT-large-v1.1 0.3099 0.9298 0.1860 0.2148 0.2148 0.3834
kf-deberta-multitask 0.2281 0.6842 0.1368 0.1724 0.1724 0.2939
gte-multilingual-base 0.2865 0.8596 0.1719 0.2096 0.2096 0.3637
bge-m3 0.3099 0.9298 0.1860 0.2221 0.2221 0.3894
KoE5 0.3012 0.9035 0.1807 0.2051 0.2051 0.3697

Top-k 10

Model name F1 Recall Precision mAP mRR NDCG
paraphrase-multilingual-mpnet-base-v2 0.1212 0.6667 0.0667 0.1197 0.1197 0.2382
KoSimCSE-roberta 0.1324 0.7281 0.0728 0.1080 0.1080 0.2411
Cohere embed-multilingual-v3.0 0.1324 0.7281 0.0728 0.1150 0.1150 0.2473
openai ada 002 0.1563 0.8596 0.0860 0.1051 0.1051 0.2673
multilingual-e5-large-instruct 0.1483 0.8158 0.0816 0.0980 0.0980 0.2520
Upstage Embedding 0.1707 0.9386 0.0939 0.1078 0.1078 0.2848
paraphrase-multilingual-MiniLM-L12-v2 0.1053 0.5789 0.0579 0.0961 0.0961 0.2006
openai_embed_3_small 0.1547 0.8509 0.0851 0.0984 0.0984 0.2593
ko-sroberta-multitask 0.1276 0.7018 0.0702 0.0986 0.0986 0.2275
openai_embed_3_large 0.1643 0.9035 0.0904 0.1180 0.1180 0.2855
KU-HIAI-ONTHEIT-large-v1 0.1707 0.9386 0.0939 0.1105 0.1105 0.2860
KU-HIAI-ONTHEIT-large-v1.1 0.1722 0.9474 0.0947 0.1033 0.1033 0.2822
kf-deberta-multitask 0.1388 0.7632 0.0763 0.1 0.1 0.2422
gte-multilingual-base 0.1675 0.9211 0.0921 0.1066 0.1066 0.2805
bge-m3 0.1754 0.9649 0.0965 0.1125 0.1125 0.2939
KoE5 0.1675 0.9211 0.0921 0.0993 0.0993 0.2734

Top-k 50

Model name F1 Recall Precision mAP mRR NDCG
paraphrase-multilingual-mpnet-base-v2 0.0320 0.8158 0.0163 0.0233 0.0233 0.1529
KoSimCSE-roberta 0.0368 0.9386 0.0188 0.0270 0.0270 0.1758
Cohere embed-multilingual-v3.0 0.0382 0.9737 0.0195 0.0220 0.0220 0.1763
openai ada 002 0.0375 0.9561 0.0191 0.0295 0.0295 0.1789
multilingual-e5-large-instruct 0.0378 0.9649 0.0193 0.0295 0.0295 0.1804
Upstage Embedding 0.0392 1.0000 0.0200 0.0206 0.0206 0.1776
paraphrase-multilingual-MiniLM-L12-v2 0.0313 0.7982 0.0160 0.0218 0.0218 0.1503
openai_embed_3_small 0.0382 0.9737 0.0195 0.0202 0.0202 0.1731
ko-sroberta-multitask 0.0354 0.9035 0.0181 0.0245 0.0245 0.1691
openai_embed_3_large 0.0382 0.9737 0.0195 0.0210 0.0210 0.1741
KU-HIAI-ONTHEIT-large-v1 0.0385 0.9825 0.0196 0.0212 0.0212 0.1758
KU-HIAI-ONTHEIT-large-v1.1 0.0385 0.9825 0.0196 0.0206 0.0206 0.1750
kf-deberta-multitask 0.0351 0.8947 0.0179 0.0228 0.0228 0.1654
gte-multilingual-base 0.0392 1.0000 0.0200 0.0259 0.0259 0.1834
bge-m3 0.0392 1.0000 0.0200 0.0206 0.0206 0.1775
KoE5 0.0385 0.9824 0.0196 0.0208 0.0208 0.1752

Installation

pip install -r requirements.txt

Running the project

  1. Make .env file using .env.template file. You have to prepare three api keys, openai, cohere, and upstage.
  2. Run evaluator with the following command.
python main.py --project_dir ./project_dir
  1. Check the result in the project_dir folder.