--- 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: - multilingual 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: 970049 num_examples: 50 - name: NanoClimateFEVER num_bytes: 596421 num_examples: 50 - name: NanoDBPedia num_bytes: 865469 num_examples: 50 - name: NanoFEVER num_bytes: 592105 num_examples: 50 - name: NanoFiQA2018 num_bytes: 246118 num_examples: 50 - name: NanoHotpotQA num_bytes: 289108 num_examples: 50 - name: NanoMSMARCO num_bytes: 273956 num_examples: 50 - name: NanoNFCorpus num_bytes: 300060 num_examples: 50 - name: NanoNQ num_bytes: 335164 num_examples: 50 - name: NanoQuoraRetrieval num_bytes: 244396 num_examples: 50 - name: NanoSCIDOCS num_bytes: 1102400 num_examples: 50 - name: NanoSciFact num_bytes: 289797 num_examples: 50 - name: NanoTouche2020 num_bytes: 1052609 num_examples: 49 download_size: 7175876 dataset_size: 7157650 - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: NanoArguAna num_bytes: 3854759 num_examples: 3635 - name: NanoClimateFEVER num_bytes: 5513610 num_examples: 3408 - name: NanoDBPedia num_bytes: 2333296 num_examples: 6045 - name: NanoFEVER num_bytes: 6185496 num_examples: 4996 - name: NanoFiQA2018 num_bytes: 4387013 num_examples: 4598 - name: NanoHotpotQA num_bytes: 1921151 num_examples: 5090 - name: NanoMSMARCO num_bytes: 1789614 num_examples: 5043 - name: NanoNFCorpus num_bytes: 4645636 num_examples: 2953 - name: NanoNQ num_bytes: 2739595 num_examples: 5035 - name: NanoQuoraRetrieval num_bytes: 371210 num_examples: 5046 - name: NanoSCIDOCS num_bytes: 2244517 num_examples: 2210 - name: NanoSciFact num_bytes: 4322717 num_examples: 2919 - name: NanoTouche2020 num_bytes: 12647783 num_examples: 5745 download_size: 33326387 dataset_size: 52956397 - config_name: harrier_oss_v1_270m features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoArguAna num_bytes: 971109 num_examples: 50 - name: NanoClimateFEVER num_bytes: 654597 num_examples: 50 - name: NanoDBPedia num_bytes: 879645 num_examples: 50 - name: NanoFEVER num_bytes: 594152 num_examples: 50 - name: NanoFiQA2018 num_bytes: 245402 num_examples: 50 - name: NanoHotpotQA num_bytes: 289260 num_examples: 50 - name: NanoMSMARCO num_bytes: 272614 num_examples: 50 - name: NanoNFCorpus num_bytes: 299623 num_examples: 50 - name: NanoNQ num_bytes: 336410 num_examples: 50 - name: NanoQuoraRetrieval num_bytes: 244260 num_examples: 50 - name: NanoSCIDOCS num_bytes: 1102400 num_examples: 50 - name: NanoSciFact num_bytes: 290926 num_examples: 50 - name: NanoTouche2020 num_bytes: 1052535 num_examples: 49 download_size: 7251332 dataset_size: 7232933 - 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: 62708 num_examples: 50 - name: NanoClimateFEVER num_bytes: 7503 num_examples: 50 - name: NanoDBPedia num_bytes: 3190 num_examples: 50 - name: NanoFEVER num_bytes: 3027 num_examples: 50 - name: NanoFiQA2018 num_bytes: 3869 num_examples: 50 - name: NanoHotpotQA num_bytes: 6007 num_examples: 50 - name: NanoMSMARCO num_bytes: 2551 num_examples: 50 - name: NanoNFCorpus num_bytes: 2065 num_examples: 50 - name: NanoNQ num_bytes: 3114 num_examples: 50 - name: NanoQuoraRetrieval num_bytes: 3218 num_examples: 50 - name: NanoSCIDOCS num_bytes: 6349 num_examples: 50 - name: NanoSciFact num_bytes: 5496 num_examples: 50 - name: NanoTouche2020 num_bytes: 3223 num_examples: 49 download_size: 104544 dataset_size: 112320 - config_name: reranking_hybrid features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoArguAna num_bytes: 194092 num_examples: 50 - name: NanoClimateFEVER num_bytes: 133207 num_examples: 50 - name: NanoDBPedia num_bytes: 177422 num_examples: 50 - name: NanoFEVER num_bytes: 120343 num_examples: 50 - name: NanoFiQA2018 num_bytes: 49707 num_examples: 50 - name: NanoHotpotQA num_bytes: 59036 num_examples: 50 - name: NanoMSMARCO num_bytes: 55213 num_examples: 50 - name: NanoNFCorpus num_bytes: 60437 num_examples: 50 - name: NanoNQ num_bytes: 67874 num_examples: 50 - name: NanoQuoraRetrieval num_bytes: 49410 num_examples: 50 - name: NanoSCIDOCS num_bytes: 222444 num_examples: 50 - name: NanoSciFact num_bytes: 58611 num_examples: 50 - name: NanoTouche2020 num_bytes: 210895 num_examples: 49 download_size: 1476363 dataset_size: 1458715 --- # NanoBEIR-sr This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench). NanoBEIR-sr is the Serbian 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-sr" 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 | 1182.9 | 1210.0 | 1432.2 | 989.8 | 885.0 | 1235.0 | | NanoClimateFEVER | 50 | 3408 | 148 | 135.2 | 129.5 | 172.8 | 1552.3 | 1403.0 | 1994.0 | | NanoDBPedia | 50 | 6045 | 1158 | 41.2 | 31.5 | 43.0 | 338.9 | 369.0 | 444.0 | | NanoFEVER | 50 | 4996 | 57 | 46.1 | 44.0 | 52.5 | 1184.6 | 1007.5 | 1580.0 | | NanoFiQA2018 | 50 | 4598 | 123 | 63.8 | 60.5 | 80.2 | 914.4 | 661.0 | 1159.8 | | NanoHotpotQA | 50 | 5090 | 100 | 86.5 | 75.5 | 105.0 | 353.6 | 306.0 | 486.0 | | NanoMSMARCO | 50 | 5043 | 50 | 35.6 | 32.0 | 43.8 | 331.1 | 300.0 | 382.0 | | NanoNFCorpus | 50 | 2953 | 2518 | 23.1 | 20.0 | 33.5 | 1522.7 | 1540.0 | 1791.0 | | NanoNQ | 50 | 5035 | 57 | 45.6 | 44.5 | 53.8 | 514.5 | 436.0 | 738.5 | | NanoQuoraRetrieval | 50 | 5046 | 70 | 49.3 | 45.0 | 54.8 | 58.1 | 49.0 | 67.0 | | NanoSCIDOCS | 50 | 2210 | 244 | 77.1 | 74.0 | 89.5 | 944.5 | 919.0 | 1236.5 | | NanoSciFact | 50 | 2919 | 56 | 96.4 | 90.0 | 126.0 | 1433.9 | 1341.0 | 1734.0 | | NanoTouche2020 | 49 | 5745 | 932 | 55.1 | 55.0 | 66.0 | 2095.8 | 977.0 | 2967.0 | ## 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 | - | 39.59 | 50.50 | 48.68 | 68.68 | 77.67 | 79.21 | - | 38 | | NanoArguAna | regex | 28.17 | 41.87 | 36.25 | 84.00 | 94.00 | 96.00 | 100-101 | 2 | | NanoClimateFEVER | regex | 23.89 | 29.46 | 32.66 | 60.10 | 61.47 | 66.40 | 100-101 | 2 | | NanoDBPedia | english_porter_stop | 47.04 | 56.93 | 55.67 | 59.82 | 75.27 | 74.97 | 100 | 0 | | NanoFEVER | regex | 64.86 | 76.11 | 71.91 | 89.33 | 90.00 | 95.00 | 100-101 | 2 | | NanoFiQA2018 | regex | 19.04 | 30.94 | 31.83 | 49.02 | 67.88 | 65.31 | 100-101 | 10 | | NanoHotpotQA | regex | 63.27 | 75.16 | 74.14 | 87.00 | 95.00 | 96.00 | 100 | 0 | | NanoMSMARCO | regex | 28.33 | 45.41 | 40.72 | 76.00 | 92.00 | 90.00 | 100-101 | 5 | | NanoNFCorpus | regex@regex | 17.76 | 24.11 | 23.01 | 12.30 | 18.76 | 20.87 | 100-101 | 9 | | NanoNQ | regex | 26.24 | 53.43 | 42.28 | 76.00 | 88.00 | 92.00 | 100-101 | 3 | | NanoQuoraRetrieval | regex | 58.37 | 81.00 | 71.29 | 93.60 | 96.00 | 97.33 | 100-101 | 1 | | NanoSCIDOCS | regex | 25.61 | 33.82 | 32.29 | 48.47 | 62.13 | 63.17 | 100-101 | 1 | | NanoSciFact | regex | 64.68 | 62.23 | 68.34 | 88.00 | 87.00 | 93.00 | 100-101 | 3 | | NanoTouche2020 | regex | 47.41 | 46.05 | 52.46 | 69.16 | 82.14 | 79.65 | 100 | 0 | ## Hybrid Safeguard Summary - Safeguard positives: 38 - Rows limited by corpus size: 0 - Metadata file: `reranking_hybrid_metadata.json` ## Source Links - Original dataset: [Serbian-AI-Society/NanoBEIR-sr](https://huggingface.co/datasets/Serbian-AI-Society/NanoBEIR-sr) - Final dataset: [hakari-bench/NanoBEIR-sr](https://huggingface.co/datasets/hakari-bench/NanoBEIR-sr) ## License NanoBEIR-sr is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream source datasets.