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NanoR2MED

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information-retrieval
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nano
bm25
dense-retrieval
reranking
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metadata 
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
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        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
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        num_examples: 129
      - name: NanoR2MEDMedQADiag
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        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
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        num_examples: 522
      - name: NanoR2MEDMedXpertQAExam
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        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.

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 

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

License

NanoR2MED is a derived dataset. Users must comply with the licenses, terms, and attribution requirements of the upstream datasets and benchmarks.