README.md

---
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
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    num_examples: 129
  - name: NanoR2MEDMedQADiag
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    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
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    num_examples: 97
  - name: NanoR2MEDMedicalSciences
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    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
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    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
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    num_examples: 77
  - name: NanoR2MEDBiology
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    num_examples: 103
  - name: NanoR2MEDIIYiClinical
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    num_examples: 129
  - name: NanoR2MEDMedQADiag
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    num_examples: 118
  - name: NanoR2MEDMedXpertQAExam
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    num_examples: 97
  - name: NanoR2MEDMedicalSciences
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    num_examples: 88
  - name: NanoR2MEDPMCClinical
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    num_examples: 114
  - name: NanoR2MEDPMCTreatment
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    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
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    num_examples: 103
  - name: NanoR2MEDIIYiClinical
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    num_examples: 129
  - name: NanoR2MEDMedQADiag
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    num_examples: 118
  - name: NanoR2MEDMedXpertQAExam
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    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.