AutoRAG-example-tokenizer-benchmark

This is a benchmark of Korean tokenizers at BM25 retriever. With AutoRAG, you can make this kind of benchmark easy and fast.

Result

Module Name	F1 Score	Recall	Precision	mAP	NDCG
ko_kkma	0.7544	0.7544	0.7544	0.7544	0.7544
ko_kiwi	0.7281	0.7281	0.7281	0.7281	0.7281
space	0.6667	0.6667	0.6667	0.6667	0.6667
ko_okt	0.7982	0.7982	0.7982	0.7982	0.7982
upstage_embed	0.6667	0.6667	0.6667	0.6667	0.6667

Module Name	F1 Score	Recall	Precision	mAP	NDCG
ko_kkma	0.4649	0.9298	0.3099	0.8319	0.8570
ko_kiwi	0.4430	0.8860	0.2953	0.7968	0.8197
space	0.4167	0.8333	0.2778	0.7383	0.7626
ko_okt	0.4781	0.9561	0.3187	0.8684	0.8910
upstage_embed	0.4298	0.8596	0.2865	0.3582	0.4842

Module Name	F1 Score	Recall	Precision	mAP	NDCG
ko_kkma	0.3216	0.9649	0.1930	0.8402	0.8718
ko_kiwi	0.3158	0.9474	0.1895	0.8108	0.8449
space	0.2836	0.8509	0.1702	0.7418	0.7694
ko_okt	0.3216	0.9649	0.1930	0.8706	0.8948
upstage_embed	0.3041	0.9123	0.1825	0.2232	0.3862

Module Name	F1 Score	Recall	Precision	mAP	NDCG
ko_kkma	0.1770	0.9737	0.0974	0.8417	0.8749
ko_kiwi	0.1754	0.9649	0.0965	0.8129	0.8504
space	0.1659	0.9123	0.0912	0.7509	0.7901
ko_okt	0.1786	0.9825	0.0982	0.8731	0.9005
upstage_embed	0.1738	0.9561	0.0956	0.1094	0.2898

Module Name	F1 Score	Recall	Precision	mAP	NDCG
ko_kkma	0.0392	1.0000	0.0200	0.8427	0.8804
ko_kiwi	0.0389	0.9912	0.0198	0.8144	0.8566
space	0.0372	0.9474	0.0189	0.7532	0.7988
ko_okt	0.0392	1.0000	0.0200	0.8743	0.9050
upstage_embed	0.0392	1.0000	0.0200	0.0206	0.1776

pip install -r requirements.txt

Download dataset to data folder.
Make .env file using .env.template file. (You have to specify Upstage API key)
Run evaluator with the following command.

python3 main.py

You can check the config file at config folder. (tokenizer_benchmark.yaml)

And you can specify project dir if you want.