--- configs: - config_name: bm25 data_files: - split: NanoArguAna path: bm25/NanoArguAna-* - split: NanoClimateFEVER path: bm25/NanoClimateFEVER-* - split: NanoDBPedia path: bm25/NanoDBPedia-* - split: NanoFEVER path: bm25/NanoFEVER-* - split: NanoFiQA2018 path: bm25/NanoFiQA2018-* - split: NanoHotpotQA path: bm25/NanoHotpotQA-* - split: NanoMSMARCO path: bm25/NanoMSMARCO-* - split: NanoNFCorpus path: bm25/NanoNFCorpus-* - split: NanoNQ path: bm25/NanoNQ-* - split: NanoQuoraRetrieval path: bm25/NanoQuoraRetrieval-* - split: NanoSCIDOCS path: bm25/NanoSCIDOCS-* - split: NanoSciFact path: bm25/NanoSciFact-* - split: NanoTouche2020 path: bm25/NanoTouche2020-* - config_name: corpus data_files: - split: NanoArguAna path: corpus/NanoArguAna-* - split: NanoClimateFEVER path: corpus/NanoClimateFEVER-* - split: NanoDBPedia path: corpus/NanoDBPedia-* - split: NanoFEVER path: corpus/NanoFEVER-* - split: NanoFiQA2018 path: corpus/NanoFiQA2018-* - split: NanoHotpotQA path: corpus/NanoHotpotQA-* - split: NanoMSMARCO path: corpus/NanoMSMARCO-* - split: NanoNFCorpus path: corpus/NanoNFCorpus-* - split: NanoNQ path: corpus/NanoNQ-* - split: NanoQuoraRetrieval path: corpus/NanoQuoraRetrieval-* - split: NanoSCIDOCS path: corpus/NanoSCIDOCS-* - split: NanoSciFact path: corpus/NanoSciFact-* - split: NanoTouche2020 path: corpus/NanoTouche2020-* - config_name: qrels data_files: - split: NanoArguAna path: qrels/NanoArguAna-* - split: NanoClimateFEVER path: qrels/NanoClimateFEVER-* - split: NanoDBPedia path: qrels/NanoDBPedia-* - split: NanoFEVER path: qrels/NanoFEVER-* - split: NanoFiQA2018 path: qrels/NanoFiQA2018-* - split: NanoHotpotQA path: qrels/NanoHotpotQA-* - split: NanoMSMARCO path: qrels/NanoMSMARCO-* - split: NanoNFCorpus path: qrels/NanoNFCorpus-* - split: NanoNQ path: qrels/NanoNQ-* - split: NanoQuoraRetrieval path: qrels/NanoQuoraRetrieval-* - split: NanoSCIDOCS path: qrels/NanoSCIDOCS-* - split: NanoSciFact path: qrels/NanoSciFact-* - split: NanoTouche2020 path: qrels/NanoTouche2020-* - config_name: queries data_files: - split: NanoArguAna path: queries/NanoArguAna.parquet - split: NanoClimateFEVER path: queries/NanoClimateFEVER.parquet - split: NanoDBPedia path: queries/NanoDBPedia.parquet - split: NanoFEVER path: queries/NanoFEVER.parquet - split: NanoFiQA2018 path: queries/NanoFiQA2018.parquet - split: NanoHotpotQA path: queries/NanoHotpotQA.parquet - split: NanoMSMARCO path: queries/NanoMSMARCO.parquet - split: NanoNFCorpus path: queries/NanoNFCorpus.parquet - split: NanoNQ path: queries/NanoNQ.parquet - split: NanoQuoraRetrieval path: queries/NanoQuoraRetrieval.parquet - split: NanoSCIDOCS path: queries/NanoSCIDOCS.parquet - split: NanoSciFact path: queries/NanoSciFact.parquet - split: NanoTouche2020 path: queries/NanoTouche2020.parquet language: - ko tags: - information-retrieval - retrieval - nano - bm25 - hakari-bench 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: 300176 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: 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: 42309 num_examples: 1651 - 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 --- # NanoBEIR-ko This dataset is a Nano-style retrieval dataset. Nano-series evaluation can be run easily with [HAKARI-Bench](https://github.com/hotchpotch/hakari-bench). NanoBEIR-ko is derived from MNanoBEIR / NanoBEIR. It follows the Hugging Face Datasets layout convention used by [sentence-transformers/NanoBEIR-en](https://huggingface.co/datasets/sentence-transformers/NanoBEIR-en): each Nano split has separate `corpus`, `queries`, and `qrels` tables, and BM25 candidates are provided separately in a `bm25` table. This layout follows the NanoBEIR-style evaluation approach summarized in [NanoBEIR](https://huggingface.co/blog/sionic-ai/eval-sionic-nano-beir). 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. ## Source Links - Final dataset: [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) ## Data Layout This dataset uses four 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` Each config has the same Nano split names. The `qrels` config is positive-only. Source rows with `score <= 0` are treated as non-relevant or hard-negative annotations and are not included in `qrels`. When the source provides such rows, their documents are preferentially used as hard negatives in the `corpus` config before generic corpus-fill documents. Source hard negatives are sampled deterministically with query round-robin so one query's negative pool does not dominate the corpus. ## Split Statistics Length statistics are computed with `len(str(text))` over the `queries` and `corpus` tables. `std` is the population standard deviation over the rows in each split. | Nano split | Queries | Corpus | Qrels | Query avg | Query std | Query median | Query p25 | Query p75 | Doc avg | Doc std | Doc median | Doc p25 | Doc p75 | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| | NanoArguAna | 50 | 3635 | 50 | 619.4 | 212.6 | 604.0 | 471.5 | 768.2 | 519.6 | 290.0 | 464.0 | 317.0 | 659.5 | | NanoClimateFEVER | 50 | 3408 | 148 | 66.0 | 26.4 | 64.5 | 44.0 | 82.8 | 779.7 | 427.6 | 704.0 | 473.0 | 1005.0 | | NanoDBPedia | 50 | 6045 | 1158 | 16.8 | 7.7 | 15.5 | 11.0 | 21.8 | 187.6 | 89.4 | 198.0 | 130.0 | 244.0 | | NanoFEVER | 50 | 4996 | 57 | 26.4 | 9.2 | 25.0 | 21.0 | 29.0 | 648.1 | 441.0 | 551.5 | 327.8 | 870.0 | | NanoFiQA2018 | 50 | 4598 | 123 | 29.6 | 11.9 | 29.5 | 20.5 | 36.8 | 490.3 | 440.7 | 361.0 | 195.2 | 622.8 | | NanoHotpotQA | 50 | 5090 | 100 | 49.5 | 18.5 | 46.0 | 34.5 | 59.8 | 197.1 | 142.0 | 170.0 | 88.0 | 269.0 | | NanoMSMARCO | 50 | 5043 | 50 | 19.1 | 11.7 | 17.0 | 11.0 | 23.8 | 169.2 | 71.0 | 152.0 | 122.0 | 202.5 | | NanoNFCorpus | 50 | 2953 | 1651 | 10.8 | 7.5 | 10.0 | 5.0 | 14.8 | 752.7 | 268.7 | 758.0 | 591.0 | 895.0 | | NanoNQ | 50 | 5035 | 57 | 29.3 | 13.7 | 25.0 | 21.0 | 31.8 | 274.2 | 227.0 | 231.0 | 93.0 | 394.5 | | NanoQuoraRetrieval | 50 | 5046 | 70 | 28.7 | 11.5 | 28.0 | 21.2 | 32.0 | 32.8 | 29.6 | 28.0 | 22.0 | 37.0 | | NanoSCIDOCS | 50 | 2210 | 244 | 32.1 | 9.0 | 30.0 | 25.0 | 39.0 | 452.8 | 339.0 | 444.0 | 281.0 | 609.8 | | NanoSciFact | 50 | 2919 | 56 | 46.3 | 18.7 | 42.0 | 32.0 | 59.2 | 723.6 | 305.0 | 679.0 | 532.0 | 869.0 | | NanoTouche2020 | 49 | 5745 | 932 | 21.7 | 6.8 | 20.0 | 17.0 | 26.0 | 1032.8 | 1085.4 | 543.0 | 176.0 | 1629.0 | ## Construction Steps This dataset is constructed as follows. 1. Use MNanoBEIR / NanoBEIR as the upstream benchmark or dataset family. 2. Load source datasets from the `hakari-bench/NanoBEIR-ko` corpus, queries, and qrels tables. 3. Source evaluation split policy: the NanoBEIR split set. 4. Create one Nano split for each selected source retrieval task. 5. Keep up to 200 eligible queries per Nano split. 6. Treat source relevance rows with `score > 0` as qrels-positive documents. If the source has no score column, treat its qrels as positive-only only when that is the source task convention. 7. Exclude source rows with `score <= 0` from `qrels`. When such rows are available for selected queries, use their documents as hard-negative corpus candidates before generic fill documents. 8. Include all qrels-positive documents for the selected queries. 9. Use the included corpus tables for each Nano split; no additional document resampling is performed. 10. Remove exact duplicate query text and document text within each split. If a removed document duplicate was referenced by qrels, the qrels row was removed. 11. Store corpus text as `title` plus body text when available. 12. Generate BM25 top-100 candidates with `wordseg:ko` tokenization. 13. If a qrels-positive document is missing from the raw BM25 result, insert it into the final `bm25` candidate list by replacing a tail non-positive candidate. The `qrels` config is positive-only. When needed for top-k reranking coverage, positive qrels are capped per query to the BM25 top-k. The `bm25` candidate subset is generated from the included corpus for each split. For top-100 reranking diagnostics, positive qrels are capped to at most 100 documents per query before BM25 positive forcing. This makes full relevant coverage possible for splits whose upstream qrels contain more than 100 positives for a single query. ## BM25 Subset Policy The `bm25` config is a candidate subset for first-stage retrieval and reranking. It is not a separate source dataset. Each row contains one query id and a ranked list of up to 100 corpus ids. BM25 candidates are generated from the selected corpus for each split. When a qrels-positive document is not present in the raw BM25 top-100 results, the missing positive is forced into the final candidate list by replacing a tail candidate that is not positive for that query. Candidate ids are kept unique after replacement. Concretely, each `bm25` row is produced by tokenizing the selected split corpus and query texts with `wordseg:ko`, ranking the corpus with BM25, then writing the ranked corpus ids as `corpus-ids` for that query. The list is a candidate subset for downstream evaluation, not a full-corpus ranking. Source hard negatives, including documents referenced by source rows with `score <= 0`, may appear in the selected corpus and can naturally appear in BM25 candidates. They are still non-relevant and are not listed in `qrels`. When source hard negatives are available, the default corpus sampling policy is query round-robin: group hard negatives by selected query, preserve source rank/order within each query, add at most one new hard negative from each query per pass, remove duplicate IDs and exact duplicate text, then fill any remaining slots from source corpus order. ## Split Mapping Each Nano split maps to one source retrieval task unless noted otherwise. | Nano split | Source task | Source dataset | Queries | Corpus | Qrels | |---|---|---|---:|---:|---:| | NanoArguAna | NanoArguAna | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 3635 | 50 | | NanoClimateFEVER | NanoClimateFEVER | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 3408 | 148 | | NanoDBPedia | NanoDBPedia | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 6045 | 1158 | | NanoFEVER | NanoFEVER | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 4996 | 57 | | NanoFiQA2018 | NanoFiQA2018 | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 4598 | 123 | | NanoHotpotQA | NanoHotpotQA | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 5090 | 100 | | NanoMSMARCO | NanoMSMARCO | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 5043 | 50 | | NanoNFCorpus | NanoNFCorpus | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 2953 | 1651 | | NanoNQ | NanoNQ | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 5035 | 57 | | NanoQuoraRetrieval | NanoQuoraRetrieval | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 5046 | 70 | | NanoSCIDOCS | NanoSCIDOCS | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 2210 | 244 | | NanoSciFact | NanoSciFact | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 50 | 2919 | 56 | | NanoTouche2020 | NanoTouche2020 | [hakari-bench/NanoBEIR-ko](https://huggingface.co/datasets/hakari-bench/NanoBEIR-ko) | 49 | 5745 | 932 | ## BM25 nDCG@10 `nDCG@10` is computed from the included BM25 ranking against the included qrels. Coverage is measured against included qrels at the same BM25 top-k used for reranking diagnostics. The included BM25 candidate subset has 100.00% query coverage and 100.00% relevant coverage for every split. | Nano split | Tokenizer | Forced BM25 positives | Query cov | Relevant cov | BM25 nDCG@10 | |---|---|---:|---:|---:|---:| | NanoArguAna | wordseg:ko | 5 | 100.00% | 100.00% | 0.3666 | | NanoClimateFEVER | wordseg:ko | 60 | 100.00% | 100.00% | 0.2457 | | NanoDBPedia | wordseg:ko | 403 | 100.00% | 100.00% | 0.5322 | | NanoFEVER | wordseg:ko | 5 | 100.00% | 100.00% | 0.5723 | | NanoFiQA2018 | wordseg:ko | 54 | 100.00% | 100.00% | 0.3433 | | NanoHotpotQA | wordseg:ko | 13 | 100.00% | 100.00% | 0.5966 | | NanoMSMARCO | wordseg:ko | 6 | 100.00% | 100.00% | 0.3320 | | NanoNFCorpus | wordseg:ko | 1420 | 100.00% | 100.00% | 0.3112 | | NanoNQ | wordseg:ko | 12 | 100.00% | 100.00% | 0.4301 | | NanoQuoraRetrieval | wordseg:ko | 2 | 100.00% | 100.00% | 0.7099 | | NanoSCIDOCS | wordseg:ko | 96 | 100.00% | 100.00% | 0.2688 | | NanoSciFact | wordseg:ko | 4 | 100.00% | 100.00% | 0.6835 | | NanoTouche2020 | wordseg:ko | 245 | 100.00% | 100.00% | 0.5034 | ## Skipped Tasks No source tasks were skipped. ## License NanoBEIR-ko is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream source datasets.