Datasets:

hakari-bench
/

NanoMedical

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.

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

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.