--- configs: - config_name: corpus data_files: - split: NanoR2MEDBioinformatics path: corpus/NanoR2MEDBioinformatics-00000-of-00001.parquet - split: NanoR2MEDBiology path: corpus/NanoR2MEDBiology-00000-of-00001.parquet - split: NanoR2MEDIIYiClinical path: corpus/NanoR2MEDIIYiClinical-00000-of-00001.parquet - split: NanoR2MEDMedQADiag path: corpus/NanoR2MEDMedQADiag-00000-of-00001.parquet - split: NanoR2MEDMedXpertQAExam path: corpus/NanoR2MEDMedXpertQAExam-00000-of-00001.parquet - split: NanoR2MEDMedicalSciences path: corpus/NanoR2MEDMedicalSciences-00000-of-00001.parquet - split: NanoR2MEDPMCClinical path: corpus/NanoR2MEDPMCClinical-00000-of-00001.parquet - split: NanoR2MEDPMCTreatment path: corpus/NanoR2MEDPMCTreatment-00000-of-00001.parquet - config_name: queries data_files: - split: NanoR2MEDBioinformatics path: queries/NanoR2MEDBioinformatics-00000-of-00001.parquet - split: NanoR2MEDBiology path: queries/NanoR2MEDBiology-00000-of-00001.parquet - split: NanoR2MEDIIYiClinical path: queries/NanoR2MEDIIYiClinical-00000-of-00001.parquet - split: NanoR2MEDMedQADiag path: queries/NanoR2MEDMedQADiag-00000-of-00001.parquet - split: NanoR2MEDMedXpertQAExam path: queries/NanoR2MEDMedXpertQAExam-00000-of-00001.parquet - split: NanoR2MEDMedicalSciences path: queries/NanoR2MEDMedicalSciences-00000-of-00001.parquet - split: NanoR2MEDPMCClinical path: queries/NanoR2MEDPMCClinical-00000-of-00001.parquet - split: NanoR2MEDPMCTreatment path: queries/NanoR2MEDPMCTreatment-00000-of-00001.parquet default: true - config_name: qrels data_files: - split: NanoR2MEDBioinformatics path: qrels/NanoR2MEDBioinformatics-00000-of-00001.parquet - split: NanoR2MEDBiology path: qrels/NanoR2MEDBiology-00000-of-00001.parquet - split: NanoR2MEDIIYiClinical path: qrels/NanoR2MEDIIYiClinical-00000-of-00001.parquet - split: NanoR2MEDMedQADiag path: qrels/NanoR2MEDMedQADiag-00000-of-00001.parquet - split: NanoR2MEDMedXpertQAExam path: qrels/NanoR2MEDMedXpertQAExam-00000-of-00001.parquet - split: NanoR2MEDMedicalSciences path: qrels/NanoR2MEDMedicalSciences-00000-of-00001.parquet - split: NanoR2MEDPMCClinical path: qrels/NanoR2MEDPMCClinical-00000-of-00001.parquet - split: NanoR2MEDPMCTreatment path: qrels/NanoR2MEDPMCTreatment-00000-of-00001.parquet - config_name: bm25 data_files: - split: NanoR2MEDBioinformatics path: bm25/NanoR2MEDBioinformatics-00000-of-00001.parquet - split: NanoR2MEDBiology path: bm25/NanoR2MEDBiology-00000-of-00001.parquet - split: NanoR2MEDIIYiClinical path: bm25/NanoR2MEDIIYiClinical-00000-of-00001.parquet - split: NanoR2MEDMedQADiag path: bm25/NanoR2MEDMedQADiag-00000-of-00001.parquet - split: NanoR2MEDMedXpertQAExam path: bm25/NanoR2MEDMedXpertQAExam-00000-of-00001.parquet - split: NanoR2MEDMedicalSciences path: bm25/NanoR2MEDMedicalSciences-00000-of-00001.parquet - split: NanoR2MEDPMCClinical path: bm25/NanoR2MEDPMCClinical-00000-of-00001.parquet - split: NanoR2MEDPMCTreatment path: bm25/NanoR2MEDPMCTreatment-00000-of-00001.parquet - config_name: harrier_oss_v1_270m data_files: - split: NanoR2MEDBioinformatics path: harrier_oss_v1_270m/NanoR2MEDBioinformatics-00000-of-00001.parquet - split: NanoR2MEDBiology path: harrier_oss_v1_270m/NanoR2MEDBiology-00000-of-00001.parquet - split: NanoR2MEDIIYiClinical path: harrier_oss_v1_270m/NanoR2MEDIIYiClinical-00000-of-00001.parquet - split: NanoR2MEDMedQADiag path: harrier_oss_v1_270m/NanoR2MEDMedQADiag-00000-of-00001.parquet - split: NanoR2MEDMedXpertQAExam path: harrier_oss_v1_270m/NanoR2MEDMedXpertQAExam-00000-of-00001.parquet - split: NanoR2MEDMedicalSciences path: harrier_oss_v1_270m/NanoR2MEDMedicalSciences-00000-of-00001.parquet - split: NanoR2MEDPMCClinical path: harrier_oss_v1_270m/NanoR2MEDPMCClinical-00000-of-00001.parquet - split: NanoR2MEDPMCTreatment path: harrier_oss_v1_270m/NanoR2MEDPMCTreatment-00000-of-00001.parquet - config_name: reranking_hybrid data_files: - split: NanoR2MEDBioinformatics path: reranking_hybrid/NanoR2MEDBioinformatics-00000-of-00001.parquet - split: NanoR2MEDBiology path: reranking_hybrid/NanoR2MEDBiology-00000-of-00001.parquet - split: NanoR2MEDIIYiClinical path: reranking_hybrid/NanoR2MEDIIYiClinical-00000-of-00001.parquet - split: NanoR2MEDMedQADiag path: reranking_hybrid/NanoR2MEDMedQADiag-00000-of-00001.parquet - split: NanoR2MEDMedXpertQAExam path: reranking_hybrid/NanoR2MEDMedXpertQAExam-00000-of-00001.parquet - split: NanoR2MEDMedicalSciences path: reranking_hybrid/NanoR2MEDMedicalSciences-00000-of-00001.parquet - split: NanoR2MEDPMCClinical path: reranking_hybrid/NanoR2MEDPMCClinical-00000-of-00001.parquet - split: NanoR2MEDPMCTreatment path: reranking_hybrid/NanoR2MEDPMCTreatment-00000-of-00001.parquet language: - en 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: NanoR2MEDBioinformatics num_bytes: 979218 num_examples: 77 - name: NanoR2MEDBiology num_bytes: 2351235 num_examples: 103 - name: NanoR2MEDIIYiClinical num_bytes: 710110 num_examples: 129 - name: NanoR2MEDMedQADiag num_bytes: 1590969 num_examples: 118 - name: NanoR2MEDMedXpertQAExam num_bytes: 1337664 num_examples: 97 - name: NanoR2MEDMedicalSciences num_bytes: 1005000 num_examples: 88 - name: NanoR2MEDPMCClinical num_bytes: 914235 num_examples: 114 - name: NanoR2MEDPMCTreatment num_bytes: 1282416 num_examples: 150 download_size: 10181648 dataset_size: 10170847 - config_name: corpus features: - name: _id dtype: string - name: text dtype: string splits: - name: NanoR2MEDBioinformatics num_bytes: 6972478 num_examples: 10000 - name: NanoR2MEDBiology num_bytes: 5244913 num_examples: 10000 - name: NanoR2MEDIIYiClinical num_bytes: 50683470 num_examples: 10000 - name: NanoR2MEDMedQADiag num_bytes: 8246495 num_examples: 10000 - name: NanoR2MEDMedXpertQAExam num_bytes: 7562082 num_examples: 10000 - name: NanoR2MEDMedicalSciences num_bytes: 7059988 num_examples: 10000 - name: NanoR2MEDPMCClinical num_bytes: 21265478 num_examples: 10000 - name: NanoR2MEDPMCTreatment num_bytes: 7492983 num_examples: 10000 download_size: 60445632 dataset_size: 114527887 - config_name: harrier_oss_v1_270m features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoR2MEDBioinformatics num_bytes: 1031642 num_examples: 77 - name: NanoR2MEDBiology num_bytes: 2344407 num_examples: 103 - name: NanoR2MEDIIYiClinical num_bytes: 708978 num_examples: 129 - name: NanoR2MEDMedQADiag num_bytes: 1597456 num_examples: 118 - name: NanoR2MEDMedXpertQAExam num_bytes: 1331623 num_examples: 97 - name: NanoR2MEDMedicalSciences num_bytes: 976991 num_examples: 88 - name: NanoR2MEDPMCClinical num_bytes: 914282 num_examples: 114 - name: NanoR2MEDPMCTreatment num_bytes: 1285806 num_examples: 150 download_size: 10202117 dataset_size: 10191185 - config_name: qrels features: - name: query-id dtype: string - name: corpus-id dtype: string splits: - name: NanoR2MEDBioinformatics num_bytes: 7391 num_examples: 226 - name: NanoR2MEDBiology num_bytes: 19919 num_examples: 374 - name: NanoR2MEDIIYiClinical num_bytes: 10032 num_examples: 457 - name: NanoR2MEDMedQADiag num_bytes: 17524 num_examples: 522 - name: NanoR2MEDMedXpertQAExam num_bytes: 11566 num_examples: 292 - name: NanoR2MEDMedicalSciences num_bytes: 8419 num_examples: 244 - name: NanoR2MEDPMCClinical num_bytes: 7933 num_examples: 248 - name: NanoR2MEDPMCTreatment num_bytes: 10728 num_examples: 315 download_size: 45781 dataset_size: 93512 - config_name: queries features: - name: _id dtype: string - name: text dtype: string splits: - name: NanoR2MEDBioinformatics num_bytes: 69335 num_examples: 77 - name: NanoR2MEDBiology num_bytes: 54909 num_examples: 103 - name: NanoR2MEDIIYiClinical num_bytes: 336780 num_examples: 129 - name: NanoR2MEDMedQADiag num_bytes: 84918 num_examples: 118 - name: NanoR2MEDMedXpertQAExam num_bytes: 92043 num_examples: 97 - name: NanoR2MEDMedicalSciences num_bytes: 42959 num_examples: 88 - name: NanoR2MEDPMCClinical num_bytes: 96689 num_examples: 114 - name: NanoR2MEDPMCTreatment num_bytes: 266871 num_examples: 150 download_size: 577548 dataset_size: 1044504 - config_name: reranking_hybrid features: - name: query-id dtype: string - name: corpus-ids list: string splits: - name: NanoR2MEDBioinformatics num_bytes: 198857 num_examples: 77 - name: NanoR2MEDBiology num_bytes: 479758 num_examples: 103 - name: NanoR2MEDIIYiClinical num_bytes: 143606 num_examples: 129 - name: NanoR2MEDMedQADiag num_bytes: 320866 num_examples: 118 - name: NanoR2MEDMedXpertQAExam num_bytes: 269347 num_examples: 97 - name: NanoR2MEDMedicalSciences num_bytes: 200396 num_examples: 88 - name: NanoR2MEDPMCClinical num_bytes: 184761 num_examples: 114 - name: NanoR2MEDPMCTreatment num_bytes: 259618 num_examples: 150 download_size: 2066678 dataset_size: 2057209 --- # NanoR2MED This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench). NanoR2MED contains 8 Nano retrieval splits derived from R2MED. 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/NanoR2MED" split = "NanoR2MEDBioinformatics" 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 | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| | NanoR2MEDBioinformatics | 77 | 10000 | 226 | 890.3 | 727.0 | 1016.0 | 666.8 | 679.0 | 798.0 | | NanoR2MEDBiology | 103 | 10000 | 374 | 523.0 | 440.0 | 627.5 | 474.1 | 397.0 | 556.0 | | NanoR2MEDIIYiClinical | 129 | 10000 | 457 | 2584.1 | 2523.0 | 3306.0 | 5042.3 | 4280.5 | 6809.5 | | NanoR2MEDMedQADiag | 118 | 10000 | 522 | 706.7 | 630.0 | 884.0 | 791.4 | 882.0 | 989.0 | | NanoR2MEDMedXpertQAExam | 97 | 10000 | 292 | 928.4 | 879.0 | 1086.0 | 723.9 | 767.0 | 922.0 | | NanoR2MEDMedicalSciences | 88 | 10000 | 244 | 477.6 | 378.0 | 596.8 | 678.6 | 679.0 | 801.0 | | NanoR2MEDPMCClinical | 114 | 10000 | 248 | 827.7 | 832.0 | 956.8 | 2103.5 | 2131.0 | 2724.0 | | NanoR2MEDPMCTreatment | 150 | 10000 | 315 | 1755.8 | 1750.5 | 1980.8 | 726.6 | 608.0 | 928.0 | ## 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 | - | 20.94 | 30.07 | 28.82 | 55.11 | 68.46 | 70.40 | - | 122 | | NanoR2MEDBioinformatics | english_porter_stop | 21.89 | 34.25 | 26.23 | 67.34 | 75.92 | 80.48 | 100-101 | 6 | | NanoR2MEDBiology | english_porter_stop | 34.55 | 49.53 | 47.22 | 70.56 | 82.57 | 85.61 | 100-101 | 3 | | NanoR2MEDIIYiClinical | english_porter_stop | 14.82 | 18.70 | 19.75 | 47.30 | 66.15 | 67.21 | 100-101 | 14 | | NanoR2MEDMedQADiag | english_porter_stop | 7.00 | 12.54 | 14.06 | 25.10 | 46.74 | 42.14 | 100-101 | 34 | | NanoR2MEDMedXpertQAExam | english_porter_stop | 2.77 | 15.99 | 9.79 | 19.94 | 47.09 | 43.55 | 100-101 | 33 | | NanoR2MEDMedicalSciences | english_porter_stop | 21.40 | 35.67 | 33.20 | 74.86 | 85.61 | 85.66 | 100-101 | 3 | | NanoR2MEDPMCClinical | english_porter_stop | 39.33 | 35.84 | 44.77 | 81.58 | 76.61 | 86.48 | 100-101 | 6 | | NanoR2MEDPMCTreatment | english_porter_stop | 25.80 | 38.01 | 35.55 | 54.18 | 66.97 | 72.04 | 100-101 | 23 | ## Hybrid Safeguard Summary - Safeguard positives: 122 - Rows limited by corpus size: 0 - Metadata file: `reranking_hybrid_metadata.json` ## Source Links - Source benchmark: `R2MED` - `R2MED/Bioinformatics`: https://huggingface.co/datasets/R2MED/Bioinformatics - `R2MED/Biology`: https://huggingface.co/datasets/R2MED/Biology - `R2MED/IIYi-Clinical`: https://huggingface.co/datasets/R2MED/IIYi-Clinical - `R2MED/MedQA-Diag`: https://huggingface.co/datasets/R2MED/MedQA-Diag - `R2MED/MedXpertQA-Exam`: https://huggingface.co/datasets/R2MED/MedXpertQA-Exam - `R2MED/Medical-Sciences`: https://huggingface.co/datasets/R2MED/Medical-Sciences - `R2MED/PMC-Clinical`: https://huggingface.co/datasets/R2MED/PMC-Clinical - `R2MED/PMC-Treatment`: https://huggingface.co/datasets/R2MED/PMC-Treatment ## License NanoR2MED is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream datasets and benchmarks.