--- configs: - config_name: corpus data_files: - split: NanoCMedQAv2reranking path: corpus/NanoCMedQAv2reranking-00000-of-00001.parquet - split: NanoCUREv1 path: corpus/NanoCUREv1-00000-of-00001.parquet - split: NanoCmedqa path: corpus/NanoCmedqa-00000-of-00001.parquet - split: NanoMedicalQA path: corpus/NanoMedicalQA-00000-of-00001.parquet - split: NanoNFCorpus path: corpus/NanoNFCorpus-00000-of-00001.parquet - split: NanoPublicHealthQA path: corpus/NanoPublicHealthQA-00000-of-00001.parquet - split: NanoSciFact path: corpus/NanoSciFact-00000-of-00001.parquet - split: NanoSciFactPL path: corpus/NanoSciFactPL-00000-of-00001.parquet - split: NanoTRECCOVID path: corpus/NanoTRECCOVID-00000-of-00001.parquet - split: NanoTRECCOVIDPL path: corpus/NanoTRECCOVIDPL-00000-of-00001.parquet - config_name: queries data_files: - split: NanoCMedQAv2reranking path: queries/NanoCMedQAv2reranking-00000-of-00001.parquet - split: NanoCUREv1 path: queries/NanoCUREv1-00000-of-00001.parquet - split: NanoCmedqa path: queries/NanoCmedqa-00000-of-00001.parquet - split: NanoMedicalQA path: queries/NanoMedicalQA-00000-of-00001.parquet - split: NanoNFCorpus path: queries/NanoNFCorpus-00000-of-00001.parquet - split: NanoPublicHealthQA path: queries/NanoPublicHealthQA-00000-of-00001.parquet - split: NanoSciFact path: queries/NanoSciFact-00000-of-00001.parquet - split: NanoSciFactPL path: queries/NanoSciFactPL-00000-of-00001.parquet - split: NanoTRECCOVID path: queries/NanoTRECCOVID-00000-of-00001.parquet - split: NanoTRECCOVIDPL path: queries/NanoTRECCOVIDPL-00000-of-00001.parquet default: true - config_name: qrels data_files: - split: NanoCMedQAv2reranking path: qrels/NanoCMedQAv2reranking-00000-of-00001.parquet - split: NanoCUREv1 path: qrels/NanoCUREv1-00000-of-00001.parquet - split: NanoCmedqa path: qrels/NanoCmedqa-00000-of-00001.parquet - split: NanoMedicalQA path: qrels/NanoMedicalQA-00000-of-00001.parquet - split: NanoNFCorpus path: qrels/NanoNFCorpus-00000-of-00001.parquet - split: NanoPublicHealthQA path: qrels/NanoPublicHealthQA-00000-of-00001.parquet - split: NanoSciFact path: qrels/NanoSciFact-00000-of-00001.parquet - split: NanoSciFactPL path: qrels/NanoSciFactPL-00000-of-00001.parquet - split: NanoTRECCOVID path: qrels/NanoTRECCOVID-00000-of-00001.parquet - split: NanoTRECCOVIDPL path: qrels/NanoTRECCOVIDPL-00000-of-00001.parquet - config_name: bm25 data_files: - split: NanoCMedQAv2reranking path: bm25/NanoCMedQAv2reranking-00000-of-00001.parquet - split: NanoCUREv1 path: bm25/NanoCUREv1-00000-of-00001.parquet - split: NanoCmedqa path: bm25/NanoCmedqa-00000-of-00001.parquet - split: NanoMedicalQA path: bm25/NanoMedicalQA-00000-of-00001.parquet - split: NanoNFCorpus path: bm25/NanoNFCorpus-00000-of-00001.parquet - split: NanoPublicHealthQA path: bm25/NanoPublicHealthQA-00000-of-00001.parquet - split: NanoSciFact path: bm25/NanoSciFact-00000-of-00001.parquet - split: NanoSciFactPL path: bm25/NanoSciFactPL-00000-of-00001.parquet - split: NanoTRECCOVID path: bm25/NanoTRECCOVID-00000-of-00001.parquet - split: NanoTRECCOVIDPL path: bm25/NanoTRECCOVIDPL-00000-of-00001.parquet - config_name: harrier_oss_v1_270m data_files: - split: NanoCMedQAv2reranking path: harrier_oss_v1_270m/NanoCMedQAv2reranking-00000-of-00001.parquet - split: NanoCUREv1 path: harrier_oss_v1_270m/NanoCUREv1-00000-of-00001.parquet - split: NanoCmedqa path: harrier_oss_v1_270m/NanoCmedqa-00000-of-00001.parquet - split: NanoMedicalQA path: harrier_oss_v1_270m/NanoMedicalQA-00000-of-00001.parquet - split: NanoNFCorpus path: harrier_oss_v1_270m/NanoNFCorpus-00000-of-00001.parquet - split: NanoPublicHealthQA path: harrier_oss_v1_270m/NanoPublicHealthQA-00000-of-00001.parquet - split: NanoSciFact path: harrier_oss_v1_270m/NanoSciFact-00000-of-00001.parquet - split: NanoSciFactPL path: harrier_oss_v1_270m/NanoSciFactPL-00000-of-00001.parquet - split: NanoTRECCOVID path: harrier_oss_v1_270m/NanoTRECCOVID-00000-of-00001.parquet - split: NanoTRECCOVIDPL path: harrier_oss_v1_270m/NanoTRECCOVIDPL-00000-of-00001.parquet - config_name: reranking_hybrid data_files: - split: NanoCMedQAv2reranking path: reranking_hybrid/NanoCMedQAv2reranking-00000-of-00001.parquet - split: NanoCUREv1 path: reranking_hybrid/NanoCUREv1-00000-of-00001.parquet - split: NanoCmedqa path: reranking_hybrid/NanoCmedqa-00000-of-00001.parquet - split: NanoMedicalQA path: reranking_hybrid/NanoMedicalQA-00000-of-00001.parquet - split: NanoNFCorpus path: reranking_hybrid/NanoNFCorpus-00000-of-00001.parquet - split: NanoPublicHealthQA path: reranking_hybrid/NanoPublicHealthQA-00000-of-00001.parquet - split: NanoSciFact path: reranking_hybrid/NanoSciFact-00000-of-00001.parquet - split: NanoSciFactPL path: reranking_hybrid/NanoSciFactPL-00000-of-00001.parquet - split: NanoTRECCOVID path: reranking_hybrid/NanoTRECCOVID-00000-of-00001.parquet - split: NanoTRECCOVIDPL path: reranking_hybrid/NanoTRECCOVIDPL-00000-of-00001.parquet language: - ar - en - pl - zh tags: - information-retrieval - retrieval - nano - bm25 - dense-retrieval - reranking - hakari-bench dataset_info: - config_name: bm25 features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoCMedQAv2reranking num_bytes: 3101432 num_examples: 200 - name: NanoCUREv1 num_bytes: 4008800 num_examples: 200 - name: NanoCmedqa num_bytes: 3608000 num_examples: 200 - name: NanoMedicalQA num_bytes: 4008800 num_examples: 200 - name: NanoNFCorpus num_bytes: 1199880 num_examples: 200 - name: NanoPublicHealthQA num_bytes: 51848 num_examples: 86 - name: NanoSciFact num_bytes: 1163934 num_examples: 200 - name: NanoSciFactPL num_bytes: 1163174 num_examples: 200 - name: NanoTRECCOVID num_bytes: 300491 num_examples: 50 - name: NanoTRECCOVIDPL num_bytes: 300491 num_examples: 50 download_size: 18921664 dataset_size: 18906850 - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: NanoCMedQAv2reranking num_bytes: 3343865 num_examples: 10000 - name: NanoCUREv1 num_bytes: 6486485 num_examples: 10000 - name: NanoCmedqa num_bytes: 4804319 num_examples: 10000 - name: NanoMedicalQA num_bytes: 2301236 num_examples: 2007 - name: NanoNFCorpus num_bytes: 5776748 num_examples: 3593 - name: NanoPublicHealthQA num_bytes: 128026 num_examples: 86 - name: NanoSciFact num_bytes: 7859380 num_examples: 5183 - name: NanoSciFactPL num_bytes: 8532845 num_examples: 5183 - name: NanoTRECCOVID num_bytes: 12276167 num_examples: 10000 - name: NanoTRECCOVIDPL num_bytes: 13280784 num_examples: 10000 download_size: 37530994 dataset_size: 64789855 - config_name: harrier_oss_v1_270m features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoCMedQAv2reranking num_bytes: 3103157 num_examples: 200 - name: NanoCUREv1 num_bytes: 4008800 num_examples: 200 - name: NanoCmedqa num_bytes: 3608000 num_examples: 200 - name: NanoMedicalQA num_bytes: 4008800 num_examples: 200 - name: NanoNFCorpus num_bytes: 1197275 num_examples: 200 - name: NanoPublicHealthQA num_bytes: 51848 num_examples: 86 - name: NanoSciFact num_bytes: 1163055 num_examples: 200 - name: NanoSciFactPL num_bytes: 1162966 num_examples: 200 - name: NanoTRECCOVID num_bytes: 300491 num_examples: 50 - name: NanoTRECCOVIDPL num_bytes: 300491 num_examples: 50 download_size: 18919647 dataset_size: 18904883 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string splits: - name: NanoCMedQAv2reranking num_bytes: 18428 num_examples: 377 - name: NanoCUREv1 num_bytes: 414480 num_examples: 5181 - name: NanoCmedqa num_bytes: 23328 num_examples: 324 - name: NanoMedicalQA num_bytes: 16000 num_examples: 200 - name: NanoNFCorpus num_bytes: 93926 num_examples: 3718 - name: NanoPublicHealthQA num_bytes: 1184 num_examples: 86 - name: NanoSciFact num_bytes: 4181 num_examples: 226 - name: NanoSciFactPL num_bytes: 4181 num_examples: 226 - name: NanoTRECCOVID num_bytes: 891 num_examples: 50 - name: NanoTRECCOVIDPL num_bytes: 891 num_examples: 50 download_size: 193099 dataset_size: 577490 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: NanoCMedQAv2reranking num_bytes: 33422 num_examples: 200 - name: NanoCUREv1 num_bytes: 23978 num_examples: 200 - name: NanoCmedqa num_bytes: 38611 num_examples: 200 - name: NanoMedicalQA num_bytes: 19646 num_examples: 200 - name: NanoNFCorpus num_bytes: 6937 num_examples: 200 - name: NanoPublicHealthQA num_bytes: 13446 num_examples: 86 - name: NanoSciFact num_bytes: 20196 num_examples: 200 - name: NanoSciFactPL num_bytes: 22164 num_examples: 200 - name: NanoTRECCOVID num_bytes: 3953 num_examples: 50 - name: NanoTRECCOVIDPL num_bytes: 4133 num_examples: 50 download_size: 137084 dataset_size: 186486 - config_name: reranking_hybrid features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoCMedQAv2reranking num_bytes: 625853 num_examples: 200 - name: NanoCUREv1 num_bytes: 809360 num_examples: 200 - name: NanoCmedqa num_bytes: 730052 num_examples: 200 - name: NanoMedicalQA num_bytes: 809040 num_examples: 200 - name: NanoNFCorpus num_bytes: 242660 num_examples: 200 - name: NanoPublicHealthQA num_bytes: 51848 num_examples: 86 - name: NanoSciFact num_bytes: 234403 num_examples: 200 - name: NanoSciFactPL num_bytes: 234349 num_examples: 200 - name: NanoTRECCOVID num_bytes: 60515 num_examples: 50 - name: NanoTRECCOVIDPL num_bytes: 60515 num_examples: 50 download_size: 3870445 dataset_size: 3858595 --- # NanoMedical This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench). NanoMedical contains 10 Nano retrieval splits derived from MTEB(Medical, v1). Each split keeps up to 200 eligible queries and up to 10000 corpus documents, with exact duplicate query and document text removed where the generator records that policy. ## Usage ```python from datasets import load_dataset dataset_id = "hakari-bench/NanoMedical" split = "NanoCMedQAv2reranking" 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. ## Candidate Construction - `bm25`: local BM25 top-500 with automatic language-aware tokenization. The resolved tokenizer is shown in the Candidate Quality table, for example `wordseg@ja`. - `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. ## 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 | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| | NanoCMedQAv2reranking | 200 | 10000 | 377 | 50.1 | 39.0 | 69.2 | 100.9 | 91.0 | 128.0 | | NanoCUREv1 | 200 | 10000 | 5181 | 75.9 | 78.0 | 91.0 | 604.2 | 511.0 | 757.0 | | NanoCmedqa | 200 | 10000 | 324 | 52.0 | 44.0 | 69.0 | 157.6 | 112.0 | 203.0 | | NanoMedicalQA | 200 | 2007 | 200 | 54.2 | 50.0 | 64.0 | 1102.4 | 683.0 | 1251.0 | | NanoNFCorpus | 200 | 3593 | 3718 | 17.1 | 11.5 | 20.2 | 1589.5 | 1612.0 | 1868.0 | | NanoPublicHealthQA | 86 | 86 | 86 | 79.8 | 70.0 | 92.8 | 828.2 | 625.0 | 1041.8 | | NanoSciFact | 200 | 5183 | 226 | 90.1 | 83.0 | 107.2 | 1499.4 | 1426.0 | 1811.5 | | NanoSciFactPL | 200 | 5183 | 226 | 95.5 | 88.0 | 117.0 | 1554.5 | 1467.0 | 1871.0 | | NanoTRECCOVID | 50 | 10000 | 50 | 69.2 | 64.5 | 76.8 | 1208.8 | 1318.0 | 1735.0 | | NanoTRECCOVIDPL | 50 | 10000 | 50 | 69.4 | 66.0 | 80.2 | 1251.9 | 1360.0 | 1789.2 | ## 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 | - | 43.64 | 51.00 | 49.02 | 68.20 | 76.98 | 80.78 | - | 206 | | NanoCMedQAv2reranking | wordseg@zh | 15.27 | 32.09 | 25.29 | 34.91 | 67.21 | 62.06 | 100-101 | 59 | | NanoCUREv1 | english_porter_stop | 46.93 | 50.03 | 52.62 | 60.42 | 66.20 | 68.15 | 100-101 | 14 | | NanoCmedqa | wordseg@zh | 16.69 | 33.80 | 25.91 | 39.12 | 71.97 | 65.62 | 100-101 | 57 | | NanoMedicalQA | english_porter_stop | 54.39 | 73.08 | 65.10 | 92.00 | 92.50 | 97.00 | 100-101 | 6 | | NanoNFCorpus | english_porter_stop | 29.21 | 30.70 | 31.82 | 24.96 | 34.29 | 32.57 | 100-101 | 48 | | NanoPublicHealthQA | stemmer@arabic | 73.79 | 81.76 | 78.47 | 100.00 | 100.00 | 100.00 | 86 | 0 | | NanoSciFact | english_porter_stop | 70.17 | 73.34 | 75.06 | 94.40 | 93.25 | 97.50 | 100-101 | 5 | | NanoSciFactPL | regex | 57.50 | 60.61 | 65.38 | 86.22 | 88.40 | 92.90 | 100-101 | 13 | | NanoTRECCOVID | english_porter_stop | 39.83 | 38.75 | 31.93 | 80.00 | 70.00 | 96.00 | 100-101 | 2 | | NanoTRECCOVIDPL | regex | 32.66 | 35.85 | 38.64 | 70.00 | 86.00 | 96.00 | 100-101 | 2 | ## Hybrid Safeguard Summary - Safeguard positives: 206 - Rows limited by corpus size: 86 - Metadata file: `reranking_hybrid_metadata.json` ## Source Links - Source benchmark: `MTEB(Medical, v1)` - `clinia/CUREv1`: https://huggingface.co/datasets/clinia/CUREv1 - `mteb/CMedQAv2-reranking`: https://huggingface.co/datasets/mteb/CMedQAv2-reranking - `mteb/CmedqaRetrieval`: https://huggingface.co/datasets/mteb/CmedqaRetrieval - `mteb/SciFact-PL`: https://huggingface.co/datasets/mteb/SciFact-PL - `mteb/TRECCOVID-PL`: https://huggingface.co/datasets/mteb/TRECCOVID-PL - `mteb/medical_qa`: https://huggingface.co/datasets/mteb/medical_qa - `mteb/nfcorpus`: https://huggingface.co/datasets/mteb/nfcorpus - `mteb/scifact`: https://huggingface.co/datasets/mteb/scifact - `mteb/trec-covid`: https://huggingface.co/datasets/mteb/trec-covid - `xhluca/publichealth-qa`: https://huggingface.co/datasets/xhluca/publichealth-qa ## License NanoMedical is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream datasets and benchmarks.