| --- |
| configs: |
| - config_name: corpus |
| data_files: |
| - split: NanoArguAna |
| path: corpus/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: corpus/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: corpus/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: corpus/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: corpus/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: corpus/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: corpus/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: corpus/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: corpus/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: corpus/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: corpus/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: corpus/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: corpus/NanoTouche2020-00000-of-00001.parquet |
| - config_name: queries |
| data_files: |
| - split: NanoArguAna |
| path: queries/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: queries/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: queries/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: queries/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: queries/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: queries/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: queries/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: queries/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: queries/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: queries/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: queries/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: queries/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: queries/NanoTouche2020-00000-of-00001.parquet |
| default: true |
| - config_name: qrels |
| data_files: |
| - split: NanoArguAna |
| path: qrels/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: qrels/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: qrels/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: qrels/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: qrels/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: qrels/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: qrels/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: qrels/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: qrels/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: qrels/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: qrels/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: qrels/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: qrels/NanoTouche2020-00000-of-00001.parquet |
| - config_name: bm25 |
| data_files: |
| - split: NanoArguAna |
| path: bm25/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: bm25/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: bm25/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: bm25/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: bm25/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: bm25/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: bm25/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: bm25/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: bm25/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: bm25/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: bm25/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: bm25/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: bm25/NanoTouche2020-00000-of-00001.parquet |
| - config_name: harrier_oss_v1_270m |
| data_files: |
| - split: NanoArguAna |
| path: harrier_oss_v1_270m/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: harrier_oss_v1_270m/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: harrier_oss_v1_270m/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: harrier_oss_v1_270m/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: harrier_oss_v1_270m/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: harrier_oss_v1_270m/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: harrier_oss_v1_270m/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: harrier_oss_v1_270m/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: harrier_oss_v1_270m/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: harrier_oss_v1_270m/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: harrier_oss_v1_270m/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: harrier_oss_v1_270m/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: harrier_oss_v1_270m/NanoTouche2020-00000-of-00001.parquet |
| - config_name: reranking_hybrid |
| data_files: |
| - split: NanoArguAna |
| path: reranking_hybrid/NanoArguAna-00000-of-00001.parquet |
| - split: NanoClimateFEVER |
| path: reranking_hybrid/NanoClimateFEVER-00000-of-00001.parquet |
| - split: NanoDBPedia |
| path: reranking_hybrid/NanoDBPedia-00000-of-00001.parquet |
| - split: NanoFEVER |
| path: reranking_hybrid/NanoFEVER-00000-of-00001.parquet |
| - split: NanoFiQA2018 |
| path: reranking_hybrid/NanoFiQA2018-00000-of-00001.parquet |
| - split: NanoHotpotQA |
| path: reranking_hybrid/NanoHotpotQA-00000-of-00001.parquet |
| - split: NanoMSMARCO |
| path: reranking_hybrid/NanoMSMARCO-00000-of-00001.parquet |
| - split: NanoNFCorpus |
| path: reranking_hybrid/NanoNFCorpus-00000-of-00001.parquet |
| - split: NanoNQ |
| path: reranking_hybrid/NanoNQ-00000-of-00001.parquet |
| - split: NanoQuoraRetrieval |
| path: reranking_hybrid/NanoQuoraRetrieval-00000-of-00001.parquet |
| - split: NanoSCIDOCS |
| path: reranking_hybrid/NanoSCIDOCS-00000-of-00001.parquet |
| - split: NanoSciFact |
| path: reranking_hybrid/NanoSciFact-00000-of-00001.parquet |
| - split: NanoTouche2020 |
| path: reranking_hybrid/NanoTouche2020-00000-of-00001.parquet |
| language: |
| - ko |
| tags: |
| - information-retrieval |
| - retrieval |
| - nano |
| - bm25 |
| - hakari-bench |
| - dense-retrieval |
| - reranking |
| dataset_info: |
| - config_name: bm25 |
| features: |
| - name: query-id |
| dtype: string |
| - name: corpus-ids |
| list: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 969162 |
| num_examples: 50 |
| - name: NanoClimateFEVER |
| num_bytes: 596033 |
| num_examples: 50 |
| - name: NanoDBPedia |
| num_bytes: 874496 |
| num_examples: 50 |
| - name: NanoFEVER |
| num_bytes: 592808 |
| num_examples: 50 |
| - name: NanoFiQA2018 |
| num_bytes: 246063 |
| num_examples: 50 |
| - name: NanoHotpotQA |
| num_bytes: 288791 |
| num_examples: 50 |
| - name: NanoMSMARCO |
| num_bytes: 273333 |
| num_examples: 50 |
| - name: NanoNFCorpus |
| num_bytes: 300195 |
| num_examples: 50 |
| - name: NanoNQ |
| num_bytes: 336389 |
| num_examples: 50 |
| - name: NanoQuoraRetrieval |
| num_bytes: 244506 |
| num_examples: 50 |
| - name: NanoSCIDOCS |
| num_bytes: 1102400 |
| num_examples: 50 |
| - name: NanoSciFact |
| num_bytes: 290703 |
| num_examples: 50 |
| - name: NanoTouche2020 |
| num_bytes: 1052427 |
| num_examples: 49 |
| download_size: 7185582 |
| dataset_size: 7167287 |
| - config_name: corpus |
| features: |
| - name: _id |
| dtype: string |
| - name: text |
| dtype: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 4495539 |
| num_examples: 3635 |
| - name: NanoClimateFEVER |
| num_bytes: 6185771 |
| num_examples: 3408 |
| - name: NanoDBPedia |
| num_bytes: 2755486 |
| num_examples: 6045 |
| - name: NanoFEVER |
| num_bytes: 7383924 |
| num_examples: 4996 |
| - name: NanoFiQA2018 |
| num_bytes: 5302620 |
| num_examples: 4598 |
| - name: NanoHotpotQA |
| num_bytes: 2300632 |
| num_examples: 5090 |
| - name: NanoMSMARCO |
| num_bytes: 2033768 |
| num_examples: 5043 |
| - name: NanoNFCorpus |
| num_bytes: 4982616 |
| num_examples: 2953 |
| - name: NanoNQ |
| num_bytes: 3256050 |
| num_examples: 5035 |
| - name: NanoQuoraRetrieval |
| num_bytes: 467577 |
| num_examples: 5046 |
| - name: NanoSCIDOCS |
| num_bytes: 2416277 |
| num_examples: 2210 |
| - name: NanoSciFact |
| num_bytes: 4737764 |
| num_examples: 2919 |
| - name: NanoTouche2020 |
| num_bytes: 14124391 |
| num_examples: 5745 |
| download_size: 34208947 |
| dataset_size: 60442415 |
| - config_name: harrier_oss_v1_270m |
| features: |
| - name: query-id |
| dtype: string |
| - name: corpus-ids |
| list: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 968870 |
| num_examples: 50 |
| - name: NanoClimateFEVER |
| num_bytes: 632552 |
| num_examples: 50 |
| - name: NanoDBPedia |
| num_bytes: 870622 |
| num_examples: 50 |
| - name: NanoFEVER |
| num_bytes: 585535 |
| num_examples: 50 |
| - name: NanoFiQA2018 |
| num_bytes: 245703 |
| num_examples: 50 |
| - name: NanoHotpotQA |
| num_bytes: 289430 |
| num_examples: 50 |
| - name: NanoMSMARCO |
| num_bytes: 272604 |
| num_examples: 50 |
| - name: NanoNFCorpus |
| num_bytes: 299676 |
| num_examples: 50 |
| - name: NanoNQ |
| num_bytes: 336776 |
| num_examples: 50 |
| - name: NanoQuoraRetrieval |
| num_bytes: 244217 |
| num_examples: 50 |
| - name: NanoSCIDOCS |
| num_bytes: 1102400 |
| num_examples: 50 |
| - name: NanoSciFact |
| num_bytes: 290888 |
| num_examples: 50 |
| - name: NanoTouche2020 |
| num_bytes: 1052550 |
| num_examples: 49 |
| download_size: 7210251 |
| dataset_size: 7191823 |
| - config_name: qrels |
| features: |
| - name: query-id |
| dtype: string |
| - name: corpus-id |
| dtype: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 3496 |
| num_examples: 50 |
| - name: NanoClimateFEVER |
| num_bytes: 4361 |
| num_examples: 148 |
| - name: NanoDBPedia |
| num_bytes: 60640 |
| num_examples: 1158 |
| - name: NanoFEVER |
| num_bytes: 1630 |
| num_examples: 57 |
| - name: NanoFiQA2018 |
| num_bytes: 2200 |
| num_examples: 123 |
| - name: NanoHotpotQA |
| num_bytes: 3885 |
| num_examples: 100 |
| - name: NanoMSMARCO |
| num_bytes: 1065 |
| num_examples: 50 |
| - name: NanoNFCorpus |
| num_bytes: 64851 |
| num_examples: 2518 |
| - name: NanoNQ |
| num_bytes: 1340 |
| num_examples: 57 |
| - name: NanoQuoraRetrieval |
| num_bytes: 1359 |
| num_examples: 70 |
| - name: NanoSCIDOCS |
| num_bytes: 21472 |
| num_examples: 244 |
| - name: NanoSciFact |
| num_bytes: 1054 |
| num_examples: 56 |
| - name: NanoTouche2020 |
| num_bytes: 45452 |
| num_examples: 932 |
| download_size: 88208 |
| dataset_size: 190263 |
| - config_name: queries |
| features: |
| - name: _id |
| dtype: string |
| - name: text |
| dtype: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 73164 |
| num_examples: 50 |
| - name: NanoClimateFEVER |
| num_bytes: 8480 |
| num_examples: 50 |
| - name: NanoDBPedia |
| num_bytes: 3160 |
| num_examples: 50 |
| - name: NanoFEVER |
| num_bytes: 3798 |
| num_examples: 50 |
| - name: NanoFiQA2018 |
| num_bytes: 4200 |
| num_examples: 50 |
| - name: NanoHotpotQA |
| num_bytes: 7405 |
| num_examples: 50 |
| - name: NanoMSMARCO |
| num_bytes: 3009 |
| num_examples: 50 |
| - name: NanoNFCorpus |
| num_bytes: 2255 |
| num_examples: 50 |
| - name: NanoNQ |
| num_bytes: 4182 |
| num_examples: 50 |
| - name: NanoQuoraRetrieval |
| num_bytes: 4178 |
| num_examples: 50 |
| - name: NanoSCIDOCS |
| num_bytes: 6235 |
| num_examples: 50 |
| - name: NanoSciFact |
| num_bytes: 6032 |
| num_examples: 50 |
| - name: NanoTouche2020 |
| num_bytes: 3170 |
| num_examples: 49 |
| download_size: 111013 |
| dataset_size: 129268 |
| - config_name: reranking_hybrid |
| features: |
| - name: query-id |
| dtype: string |
| - name: corpus-ids |
| list: string |
| splits: |
| - name: NanoArguAna |
| num_bytes: 193458 |
| num_examples: 50 |
| - name: NanoClimateFEVER |
| num_bytes: 132082 |
| num_examples: 50 |
| - name: NanoDBPedia |
| num_bytes: 178704 |
| num_examples: 50 |
| - name: NanoFEVER |
| num_bytes: 120617 |
| num_examples: 50 |
| - name: NanoFiQA2018 |
| num_bytes: 49694 |
| num_examples: 50 |
| - name: NanoHotpotQA |
| num_bytes: 59007 |
| num_examples: 50 |
| - name: NanoMSMARCO |
| num_bytes: 55163 |
| num_examples: 50 |
| - name: NanoNFCorpus |
| num_bytes: 60686 |
| num_examples: 50 |
| - name: NanoNQ |
| num_bytes: 67880 |
| num_examples: 50 |
| - name: NanoQuoraRetrieval |
| num_bytes: 49387 |
| num_examples: 50 |
| - name: NanoSCIDOCS |
| num_bytes: 222444 |
| num_examples: 50 |
| - name: NanoSciFact |
| num_bytes: 58699 |
| num_examples: 50 |
| - name: NanoTouche2020 |
| num_bytes: 210883 |
| num_examples: 49 |
| download_size: 1476357 |
| dataset_size: 1458705 |
| --- |
| # NanoBEIR-ko |
|
|
| This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench). |
|
|
| NanoBEIR-ko is the Korean language-specific component of MNanoBEIR. It groups compact BEIR-derived retrieval tasks for efficient evaluation of document ranking in that language. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset_id = "hakari-bench/NanoBEIR-ko" |
| split = "NanoArguAna" |
| |
| queries = load_dataset(dataset_id, "queries", split=split) |
| corpus = load_dataset(dataset_id, "corpus", split=split) |
| qrels = load_dataset(dataset_id, "qrels", split=split) |
| reranking_candidates = load_dataset(dataset_id, "reranking_hybrid", split=split) |
| ``` |
|
|
| ## Data Layout |
|
|
| This dataset uses six Hugging Face Datasets configs: |
|
|
| - `corpus`: documents with `_id` and `text` |
| - `queries`: queries with `_id` and `text` |
| - `qrels`: positive relevance labels with `query-id` and `corpus-id` |
| - `bm25`: BM25 candidate lists with `query-id` and `corpus-ids` |
| - `harrier_oss_v1_270m`: dense candidate lists from `microsoft/harrier-oss-v1-270m` |
| - `reranking_hybrid`: RRF candidate lists built from `bm25` and `harrier_oss_v1_270m` |
|
|
| Each config has the same Nano split names. `NanoNFCorpus` includes the full positive qrels (2,518 rows); qrels are not capped to the top-100 reranking depth. |
|
|
| ## Candidate Construction |
|
|
| - `bm25`: local BM25 top-500 with automatic tokenizer selection. Auto mode uses `wordseg` for `ja`, `zh`, `th`, `ko`, and `vi`, and `regex` otherwise. The resolved tokenizer is shown for each split in the Candidate Quality table. |
| - `harrier_oss_v1_270m`: dense top-500 from `microsoft/harrier-oss-v1-270m`. In tables this is shown as `Dense`; Dense means `microsoft/harrier-oss-v1-270m` with the `web_search_query` prompt for queries and cosine similarity over normalized embeddings. |
| - `reranking_hybrid`: RRF over `bm25` and `harrier_oss_v1_270m` using `rrf_k=100`, keeping the RRF top-100. |
|
|
| Safeguard means rank 101 is appended only when RRF top-100 contains no qrels-positive document. Qrels are not capped to fit the top-100 reranking depth. For `NanoNFCorpus`, some queries have more than 100 positive qrels, so top-100 hybrid candidate coverage is expected to be below 100%; this is a candidate-list diagnostic, not a qrels filtering rule. |
|
|
| ## Split Statistics |
|
|
| Length statistics are character counts computed with `len(str(text))`. |
|
|
| | Nano split | Queries | Corpus | Qrels | Query chars avg | Query chars p50 | Query chars p75 | Doc chars avg | Doc chars p50 | Doc chars p75 | |
| |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| |
| | NanoArguAna | 50 | 3635 | 50 | 619.4 | 604.0 | 768.2 | 519.6 | 464.0 | 659.5 | |
| | NanoClimateFEVER | 50 | 3408 | 148 | 66.0 | 64.5 | 82.8 | 779.7 | 704.0 | 1005.0 | |
| | NanoDBPedia | 50 | 6045 | 1158 | 16.8 | 15.5 | 21.8 | 187.6 | 198.0 | 244.0 | |
| | NanoFEVER | 50 | 4996 | 57 | 26.4 | 25.0 | 29.0 | 648.1 | 551.5 | 870.0 | |
| | NanoFiQA2018 | 50 | 4598 | 123 | 29.6 | 29.5 | 36.8 | 490.3 | 361.0 | 622.8 | |
| | NanoHotpotQA | 50 | 5090 | 100 | 49.5 | 46.0 | 59.8 | 197.1 | 170.0 | 269.0 | |
| | NanoMSMARCO | 50 | 5043 | 50 | 19.1 | 17.0 | 23.8 | 169.2 | 152.0 | 202.5 | |
| | NanoNFCorpus | 50 | 2953 | 2518 | 10.8 | 10.0 | 14.8 | 752.7 | 758.0 | 895.0 | |
| | NanoNQ | 50 | 5035 | 57 | 29.3 | 25.0 | 31.8 | 274.2 | 231.0 | 394.5 | |
| | NanoQuoraRetrieval | 50 | 5046 | 70 | 28.7 | 28.0 | 32.0 | 32.8 | 28.0 | 37.0 | |
| | NanoSCIDOCS | 50 | 2210 | 244 | 32.1 | 30.0 | 39.0 | 452.8 | 444.0 | 609.8 | |
| | NanoSciFact | 50 | 2919 | 56 | 46.3 | 42.0 | 59.2 | 723.6 | 679.0 | 869.0 | |
| | NanoTouche2020 | 49 | 5745 | 932 | 21.7 | 20.0 | 26.0 | 1032.8 | 543.0 | 1629.0 | |
|
|
| ## Candidate Quality |
|
|
| `nDCG@10` and `Recall@100` are computed from the included candidate rankings against the included qrels, then reported as 0-100 scores such as `52.45`. `Recall@100` uses only the top 100 candidates; an optional rank-101 safeguard positive is not counted in `Recall@100`. |
|
|
| Dense means `microsoft/harrier-oss-v1-270m` with the `web_search_query` prompt and cosine similarity. |
|
|
| | Nano split | BM25 tokenizer | BM25 nDCG@10 | Dense nDCG@10 | Hybrid nDCG@10 | BM25 Recall@100 | Dense Recall@100 | Hybrid Recall@100 | Hybrid candidates | Safeguard positives | |
| |---|---|---:|---:|---:|---:|---:|---:|---:|---:| |
| | Mean | - | 44.99 | 50.02 | 50.47 | 74.95 | 78.50 | 81.34 | - | 31 | |
| | NanoArguAna | wordseg@ko | 36.61 | 40.82 | 42.17 | 90.00 | 94.00 | 96.00 | 100-101 | 2 | |
| | NanoClimateFEVER | wordseg@ko | 24.57 | 30.03 | 29.83 | 63.87 | 68.30 | 66.10 | 100-101 | 3 | |
| | NanoDBPedia | wordseg@ko | 53.22 | 59.28 | 57.87 | 72.53 | 76.53 | 79.55 | 100 | 0 | |
| | NanoFEVER | wordseg@ko | 57.23 | 73.35 | 70.01 | 92.00 | 98.33 | 99.00 | 100 | 0 | |
| | NanoFiQA2018 | wordseg@ko | 34.15 | 37.13 | 42.91 | 60.57 | 74.03 | 73.29 | 100-101 | 7 | |
| | NanoHotpotQA | wordseg@ko | 59.66 | 62.69 | 63.16 | 87.00 | 84.00 | 93.00 | 100-101 | 2 | |
| | NanoMSMARCO | wordseg@ko | 33.20 | 41.64 | 43.71 | 88.00 | 96.00 | 96.00 | 100-101 | 2 | |
| | NanoNFCorpus | wordseg@ko | 27.19 | 25.15 | 27.45 | 17.45 | 20.08 | 23.38 | 100-101 | 9 | |
| | NanoNQ | wordseg@ko | 43.01 | 58.05 | 50.33 | 78.00 | 93.00 | 99.00 | 100 | 0 | |
| | NanoQuoraRetrieval | wordseg@ko | 70.62 | 81.33 | 76.32 | 97.33 | 96.00 | 100.00 | 100 | 0 | |
| | NanoSCIDOCS | wordseg@ko | 26.73 | 33.10 | 33.80 | 60.93 | 64.17 | 64.27 | 100-101 | 1 | |
| | NanoSciFact | wordseg@ko | 68.35 | 62.07 | 68.38 | 92.00 | 84.00 | 90.00 | 100-101 | 5 | |
| | NanoTouche2020 | wordseg@ko | 50.33 | 45.64 | 50.13 | 74.71 | 72.08 | 77.89 | 100 | 0 | |
|
|
| ## Hybrid Safeguard Summary |
|
|
| - Safeguard positives: 31 |
| - Rows limited by corpus size: 0 |
| - Metadata file: `reranking_hybrid_metadata.json` |
|
|
| ## Source Links |
|
|
| - Final dataset: [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) |
|
|
| ## License |
|
|
| NanoBEIR-ko is a derived dataset. Users must comply with the licenses, |
| terms, and attribution requirements of the upstream source datasets. |
|
|
