--- 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: - en 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: 968728 num_examples: 50 - name: NanoClimateFEVER num_bytes: 600802 num_examples: 50 - name: NanoDBPedia num_bytes: 874981 num_examples: 50 - name: NanoFEVER num_bytes: 593474 num_examples: 50 - name: NanoFiQA2018 num_bytes: 245847 num_examples: 50 - name: NanoHotpotQA num_bytes: 289577 num_examples: 50 - name: NanoMSMARCO num_bytes: 274039 num_examples: 50 - name: NanoNFCorpus num_bytes: 300134 num_examples: 50 - name: NanoNQ num_bytes: 334792 num_examples: 50 - name: NanoQuoraRetrieval num_bytes: 245563 num_examples: 50 - name: NanoSCIDOCS num_bytes: 1102400 num_examples: 50 - name: NanoSciFact num_bytes: 290615 num_examples: 50 - name: NanoTouche2020 num_bytes: 1052535 num_examples: 49 download_size: 7191734 dataset_size: 7173508 - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: NanoArguAna num_bytes: 3854860 num_examples: 3635 - name: NanoClimateFEVER num_bytes: 5617105 num_examples: 3408 - name: NanoDBPedia num_bytes: 2280448 num_examples: 6045 - name: NanoFEVER num_bytes: 6285894 num_examples: 4996 - name: NanoFiQA2018 num_bytes: 4201493 num_examples: 4598 - name: NanoHotpotQA num_bytes: 1868234 num_examples: 5090 - name: NanoMSMARCO num_bytes: 1745074 num_examples: 5043 - name: NanoNFCorpus num_bytes: 4521394 num_examples: 2953 - name: NanoNQ num_bytes: 2740852 num_examples: 5035 - name: NanoQuoraRetrieval num_bytes: 346228 num_examples: 5046 - name: NanoSCIDOCS num_bytes: 2149671 num_examples: 2210 - name: NanoSciFact num_bytes: 4227131 num_examples: 2919 - name: NanoTouche2020 num_bytes: 12592148 num_examples: 5745 download_size: 30724168 dataset_size: 52430532 - config_name: harrier_oss_v1_270m features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoArguAna num_bytes: 969492 num_examples: 50 - name: NanoClimateFEVER num_bytes: 669504 num_examples: 50 - name: NanoDBPedia num_bytes: 892493 num_examples: 50 - name: NanoFEVER num_bytes: 596334 num_examples: 50 - name: NanoFiQA2018 num_bytes: 245652 num_examples: 50 - name: NanoHotpotQA num_bytes: 289712 num_examples: 50 - name: NanoMSMARCO num_bytes: 272519 num_examples: 50 - name: NanoNFCorpus num_bytes: 299748 num_examples: 50 - name: NanoNQ num_bytes: 336747 num_examples: 50 - name: NanoQuoraRetrieval num_bytes: 244373 num_examples: 50 - name: NanoSCIDOCS num_bytes: 1102400 num_examples: 50 - name: NanoSciFact num_bytes: 291249 num_examples: 50 - name: NanoTouche2020 num_bytes: 1052515 num_examples: 49 download_size: 7281112 dataset_size: 7262738 - 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: 62331 num_examples: 50 - name: NanoClimateFEVER num_bytes: 7044 num_examples: 50 - name: NanoDBPedia num_bytes: 2723 num_examples: 50 - name: NanoFEVER num_bytes: 2948 num_examples: 50 - name: NanoFiQA2018 num_bytes: 3531 num_examples: 50 - name: NanoHotpotQA num_bytes: 6019 num_examples: 50 - name: NanoMSMARCO num_bytes: 2328 num_examples: 50 - name: NanoNFCorpus num_bytes: 1939 num_examples: 50 - name: NanoNQ num_bytes: 3132 num_examples: 50 - name: NanoQuoraRetrieval num_bytes: 3087 num_examples: 50 - name: NanoSCIDOCS num_bytes: 6041 num_examples: 50 - name: NanoSciFact num_bytes: 5352 num_examples: 50 - name: NanoTouche2020 num_bytes: 2609 num_examples: 49 download_size: 97288 dataset_size: 109084 - config_name: reranking_hybrid features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoArguAna num_bytes: 193478 num_examples: 50 - name: NanoClimateFEVER num_bytes: 136455 num_examples: 50 - name: NanoDBPedia num_bytes: 180273 num_examples: 50 - name: NanoFEVER num_bytes: 120469 num_examples: 50 - name: NanoFiQA2018 num_bytes: 49621 num_examples: 50 - name: NanoHotpotQA num_bytes: 59078 num_examples: 50 - name: NanoMSMARCO num_bytes: 55157 num_examples: 50 - name: NanoNFCorpus num_bytes: 60573 num_examples: 50 - name: NanoNQ num_bytes: 67868 num_examples: 50 - name: NanoQuoraRetrieval num_bytes: 49452 num_examples: 50 - name: NanoSCIDOCS num_bytes: 222444 num_examples: 50 - name: NanoSciFact num_bytes: 58623 num_examples: 50 - name: NanoTouche2020 num_bytes: 210905 num_examples: 49 download_size: 1482130 dataset_size: 1464448 --- # NanoBEIR-en This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench). NanoBEIR-en is a compact English benchmark derived from BEIR retrieval datasets. It keeps the query-corpus-qrels retrieval format while using small task splits for fast, repeatable evaluation. ## Usage ```python from datasets import load_dataset dataset_id = "hakari-bench/NanoBEIR-en" 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 | 1201.8 | 1170.5 | 1446.5 | 1011.8 | 904.0 | 1259.0 | | NanoClimateFEVER | 50 | 3408 | 148 | 128.4 | 124.0 | 158.0 | 1619.5 | 1461.0 | 2079.8 | | NanoDBPedia | 50 | 6045 | 1158 | 33.1 | 33.5 | 45.5 | 336.3 | 369.0 | 445.0 | | NanoFEVER | 50 | 4996 | 57 | 45.4 | 43.0 | 53.0 | 1228.7 | 1041.0 | 1649.0 | | NanoFiQA2018 | 50 | 4598 | 123 | 58.5 | 55.0 | 75.2 | 899.6 | 651.0 | 1136.8 | | NanoHotpotQA | 50 | 5090 | 100 | 88.3 | 82.5 | 107.0 | 349.6 | 299.0 | 479.0 | | NanoMSMARCO | 50 | 5043 | 50 | 32.2 | 29.0 | 40.0 | 330.2 | 298.0 | 380.0 | | NanoNFCorpus | 50 | 2953 | 2518 | 21.0 | 16.5 | 31.5 | 1512.7 | 1532.0 | 1775.0 | | NanoNQ | 50 | 5035 | 57 | 47.0 | 42.5 | 53.0 | 525.6 | 443.0 | 758.0 | | NanoQuoraRetrieval | 50 | 5046 | 70 | 48.0 | 43.5 | 54.8 | 54.8 | 47.0 | 64.0 | | NanoSCIDOCS | 50 | 2210 | 244 | 72.8 | 71.5 | 81.8 | 923.6 | 910.5 | 1230.5 | | NanoSciFact | 50 | 2919 | 56 | 95.8 | 92.5 | 124.8 | 1431.2 | 1344.0 | 1725.0 | | NanoTouche2020 | 49 | 5745 | 932 | 43.4 | 40.0 | 57.0 | 2142.6 | 989.0 | 3032.0 | ## 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 | - | 57.34 | 61.06 | 61.80 | 81.58 | 84.35 | 86.43 | - | 13 | | NanoArguAna | english_porter_stop | 46.50 | 57.87 | 54.22 | 100.00 | 94.00 | 100.00 | 100 | 0 | | NanoClimateFEVER | english_porter_stop | 32.66 | 28.11 | 34.19 | 60.50 | 72.83 | 74.33 | 100-101 | 1 | | NanoDBPedia | english_porter_stop | 63.74 | 62.43 | 65.64 | 77.87 | 79.94 | 85.56 | 100 | 0 | | NanoFEVER | english_porter_stop | 81.43 | 88.16 | 85.21 | 100.00 | 98.00 | 100.00 | 100 | 0 | | NanoFiQA2018 | english_porter_stop | 42.11 | 50.11 | 51.50 | 73.51 | 77.31 | 81.59 | 100-101 | 4 | | NanoHotpotQA | english_porter_stop | 82.70 | 80.43 | 83.25 | 96.00 | 91.00 | 97.00 | 100 | 0 | | NanoMSMARCO | english_porter_stop | 52.17 | 61.88 | 61.70 | 100.00 | 100.00 | 100.00 | 100 | 0 | | NanoNFCorpus | regex@regex | 33.03 | 33.81 | 35.32 | 20.22 | 30.44 | 31.69 | 100-101 | 5 | | NanoNQ | english_porter_stop | 51.40 | 67.26 | 65.84 | 92.00 | 100.00 | 97.00 | 100-101 | 1 | | NanoQuoraRetrieval | english_porter_stop | 87.45 | 88.88 | 91.05 | 100.00 | 96.00 | 100.00 | 100 | 0 | | NanoSCIDOCS | english_porter_stop | 32.94 | 43.92 | 39.62 | 61.37 | 81.40 | 71.20 | 100-101 | 1 | | NanoSciFact | english_porter_stop | 72.82 | 76.79 | 73.97 | 94.00 | 92.00 | 98.00 | 100-101 | 1 | | NanoTouche2020 | english_porter_stop | 66.48 | 54.07 | 61.84 | 85.11 | 83.65 | 87.18 | 100 | 0 | ## Hybrid Safeguard Summary - Safeguard positives: 13 - Rows limited by corpus size: 0 - Metadata file: `reranking_hybrid_metadata.json` ## Source Links - Original dataset: [sentence-transformers/NanoBEIR-en](https://huggingface.co/datasets/sentence-transformers/NanoBEIR-en) - Final dataset: [hakari-bench/NanoBEIR-en](https://huggingface.co/datasets/hakari-bench/NanoBEIR-en) ## License NanoBEIR-en is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream source datasets.