README.md

---
dataset_info:
- config_name: bm25
  features:
  - name: query-id
    dtype: string
  - name: corpus-ids
    list: string
  splits:
  - name: NanoBIRCOArguAna
    num_bytes: 1870651
    num_examples: 98
  - name: NanoBIRCOClinicalTrial
    num_bytes: 375683
    num_examples: 50
  - name: NanoBIRCODorisMae
    num_bytes: 349544
    num_examples: 60
  - name: NanoBIRCORelic
    num_bytes: 637332
    num_examples: 100
  - name: NanoBIRCOWTB
    num_bytes: 763230
    num_examples: 100
  download_size: 4001233
  dataset_size: 3996440
- config_name: corpus
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: NanoBIRCOArguAna
    num_bytes: 3661676
    num_examples: 3081
  - name: NanoBIRCOClinicalTrial
    num_bytes: 4029445
    num_examples: 3375
  - name: NanoBIRCODorisMae
    num_bytes: 6851909
    num_examples: 5544
  - name: NanoBIRCORelic
    num_bytes: 2483080
    num_examples: 5023
  - name: NanoBIRCOWTB
    num_bytes: 1968736
    num_examples: 1766
  download_size: 9995334
  dataset_size: 18994846
- config_name: harrier_oss_v1_270m
  features:
  - name: query-id
    dtype: string
  - name: corpus-ids
    list: string
  splits:
  - name: NanoBIRCOArguAna
    num_bytes: 1846893
    num_examples: 98
  - name: NanoBIRCOClinicalTrial
    num_bytes: 375683
    num_examples: 50
  - name: NanoBIRCODorisMae
    num_bytes: 349899
    num_examples: 60
  - name: NanoBIRCORelic
    num_bytes: 638600
    num_examples: 100
  - name: NanoBIRCOWTB
    num_bytes: 762147
    num_examples: 100
  download_size: 3978069
  dataset_size: 3973222
- config_name: qrels
  features:
  - name: query-id
    dtype: string
  - name: corpus-id
    dtype: string
  splits:
  - name: NanoBIRCOArguAna
    num_bytes: 6906
    num_examples: 98
  - name: NanoBIRCOClinicalTrial
    num_bytes: 25649
    num_examples: 1042
  - name: NanoBIRCODorisMae
    num_bytes: 30824
    num_examples: 1569
  - name: NanoBIRCORelic
    num_bytes: 2318
    num_examples: 100
  - name: NanoBIRCOWTB
    num_bytes: 3262
    num_examples: 100
  download_size: 34194
  dataset_size: 68959
- config_name: queries
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: NanoBIRCOArguAna
    num_bytes: 114584
    num_examples: 98
  - name: NanoBIRCOClinicalTrial
    num_bytes: 25534
    num_examples: 50
  - name: NanoBIRCODorisMae
    num_bytes: 60464
    num_examples: 60
  - name: NanoBIRCORelic
    num_bytes: 103108
    num_examples: 100
  - name: NanoBIRCOWTB
    num_bytes: 83407
    num_examples: 100
  download_size: 255948
  dataset_size: 387097
- config_name: reranking_hybrid
  features:
  - name: query-id
    dtype: string
  - name: corpus-ids
    list: string
  splits:
  - name: NanoBIRCOArguAna
    num_bytes: 373151
    num_examples: 98
  - name: NanoBIRCOClinicalTrial
    num_bytes: 75698
    num_examples: 50
  - name: NanoBIRCODorisMae
    num_bytes: 70586
    num_examples: 60
  - name: NanoBIRCORelic
    num_bytes: 129194
    num_examples: 100
  - name: NanoBIRCOWTB
    num_bytes: 154507
    num_examples: 100
  download_size: 807492
  dataset_size: 803136
configs:
- config_name: corpus
  data_files:
  - split: NanoBIRCOArguAna
    path: corpus/NanoBIRCOArguAna-00000-of-00001.parquet
  - split: NanoBIRCOClinicalTrial
    path: corpus/NanoBIRCOClinicalTrial-00000-of-00001.parquet
  - split: NanoBIRCODorisMae
    path: corpus/NanoBIRCODorisMae-00000-of-00001.parquet
  - split: NanoBIRCORelic
    path: corpus/NanoBIRCORelic-00000-of-00001.parquet
  - split: NanoBIRCOWTB
    path: corpus/NanoBIRCOWTB-00000-of-00001.parquet
- config_name: queries
  data_files:
  - split: NanoBIRCOArguAna
    path: queries/NanoBIRCOArguAna-00000-of-00001.parquet
  - split: NanoBIRCOClinicalTrial
    path: queries/NanoBIRCOClinicalTrial-00000-of-00001.parquet
  - split: NanoBIRCODorisMae
    path: queries/NanoBIRCODorisMae-00000-of-00001.parquet
  - split: NanoBIRCORelic
    path: queries/NanoBIRCORelic-00000-of-00001.parquet
  - split: NanoBIRCOWTB
    path: queries/NanoBIRCOWTB-00000-of-00001.parquet
  default: true
- config_name: qrels
  data_files:
  - split: NanoBIRCOArguAna
    path: qrels/NanoBIRCOArguAna-00000-of-00001.parquet
  - split: NanoBIRCOClinicalTrial
    path: qrels/NanoBIRCOClinicalTrial-00000-of-00001.parquet
  - split: NanoBIRCODorisMae
    path: qrels/NanoBIRCODorisMae-00000-of-00001.parquet
  - split: NanoBIRCORelic
    path: qrels/NanoBIRCORelic-00000-of-00001.parquet
  - split: NanoBIRCOWTB
    path: qrels/NanoBIRCOWTB-00000-of-00001.parquet
- config_name: bm25
  data_files:
  - split: NanoBIRCOArguAna
    path: bm25/NanoBIRCOArguAna-00000-of-00001.parquet
  - split: NanoBIRCOClinicalTrial
    path: bm25/NanoBIRCOClinicalTrial-00000-of-00001.parquet
  - split: NanoBIRCODorisMae
    path: bm25/NanoBIRCODorisMae-00000-of-00001.parquet
  - split: NanoBIRCORelic
    path: bm25/NanoBIRCORelic-00000-of-00001.parquet
  - split: NanoBIRCOWTB
    path: bm25/NanoBIRCOWTB-00000-of-00001.parquet
- config_name: harrier_oss_v1_270m
  data_files:
  - split: NanoBIRCOArguAna
    path: harrier_oss_v1_270m/NanoBIRCOArguAna-00000-of-00001.parquet
  - split: NanoBIRCOClinicalTrial
    path: harrier_oss_v1_270m/NanoBIRCOClinicalTrial-00000-of-00001.parquet
  - split: NanoBIRCODorisMae
    path: harrier_oss_v1_270m/NanoBIRCODorisMae-00000-of-00001.parquet
  - split: NanoBIRCORelic
    path: harrier_oss_v1_270m/NanoBIRCORelic-00000-of-00001.parquet
  - split: NanoBIRCOWTB
    path: harrier_oss_v1_270m/NanoBIRCOWTB-00000-of-00001.parquet
- config_name: reranking_hybrid
  data_files:
  - split: NanoBIRCOArguAna
    path: reranking_hybrid/NanoBIRCOArguAna-00000-of-00001.parquet
  - split: NanoBIRCOClinicalTrial
    path: reranking_hybrid/NanoBIRCOClinicalTrial-00000-of-00001.parquet
  - split: NanoBIRCODorisMae
    path: reranking_hybrid/NanoBIRCODorisMae-00000-of-00001.parquet
  - split: NanoBIRCORelic
    path: reranking_hybrid/NanoBIRCORelic-00000-of-00001.parquet
  - split: NanoBIRCOWTB
    path: reranking_hybrid/NanoBIRCOWTB-00000-of-00001.parquet
tags:
- information-retrieval
- retrieval
- nano
- bm25
- dense-retrieval
- reranking
- hakari-bench
---
# NanoBIRCO

This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench).

NanoBIRCO contains 5 Nano retrieval splits derived from BIRCO. 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/NanoBIRCO"
split = "NanoBIRCOArguAna"

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 |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| NanoBIRCOArguAna | 98 | 3081 | 98 | 1124.0 | 1101.5 | 1410.2 | 1140.1 | 1082.0 | 1445.0 |
| NanoBIRCOClinicalTrial | 50 | 3375 | 1042 | 497.0 | 438.5 | 558.8 | 1174.3 | 1421.0 | 1579.0 |
| NanoBIRCODorisMae | 60 | 5544 | 1569 | 995.5 | 993.5 | 1095.5 | 1220.3 | 1208.5 | 1431.0 |
| NanoBIRCORelic | 100 | 5023 | 100 | 1016.3 | 1054.0 | 1144.5 | 477.3 | 438.0 | 627.0 |
| NanoBIRCOWTB | 100 | 1766 | 100 | 811.3 | 788.5 | 954.8 | 1091.2 | 1108.0 | 1269.8 |

## 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 | - | 26.93 | 29.59 | 31.11 | 63.53 | 70.96 | 73.38 | - | 66 |
| NanoBIRCOArguAna | english_porter_stop | 42.93 | 50.62 | 49.32 | 97.96 | 97.96 | 100.00 | 100 | 0 |
| NanoBIRCOClinicalTrial | english_porter_stop | 13.22 | 21.52 | 19.59 | 31.03 | 48.52 | 45.61 | 100-101 | 1 |
| NanoBIRCODorisMae | english_porter_stop | 38.66 | 41.40 | 40.12 | 70.68 | 72.34 | 80.30 | 100-101 | 6 |
| NanoBIRCORelic | english_porter_stop | 13.14 | 7.25 | 12.76 | 59.00 | 66.00 | 69.00 | 100-101 | 31 |
| NanoBIRCOWTB | english_porter_stop | 26.69 | 27.14 | 33.76 | 59.00 | 70.00 | 72.00 | 100-101 | 28 |

## Hybrid Safeguard Summary

- Safeguard positives: 66
- Rows limited by corpus size: 0
- Metadata file: `reranking_hybrid_metadata.json`

## Source Links

- Source benchmark: `BIRCO`
- Source benchmark repository: https://github.com/embeddings-benchmark/mteb
- `mteb/BIRCO-ArguAna-Test`: https://huggingface.co/datasets/mteb/BIRCO-ArguAna-Test
- `mteb/BIRCO-ClinicalTrial-Test`: https://huggingface.co/datasets/mteb/BIRCO-ClinicalTrial-Test
- `mteb/BIRCO-DorisMae-Test`: https://huggingface.co/datasets/mteb/BIRCO-DorisMae-Test
- `mteb/BIRCO-Relic-Test`: https://huggingface.co/datasets/mteb/BIRCO-Relic-Test
- `mteb/BIRCO-WTB-Test`: https://huggingface.co/datasets/mteb/BIRCO-WTB-Test

## License

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