README.md

---
configs:
- config_name: corpus
  data_files:
  - split: NanoArguAna
    path: corpus/NanoArguAna-00000-of-00001.parquet
  - split: NanoClimateFEVER
    path: corpus/NanoClimateFEVER-00000-of-00001.parquet
  - split: NanoDBPedia
    path: corpus/NanoDBPedia-00000-of-00001.parquet
  - split: NanoFEVER
    path: corpus/NanoFEVER-00000-of-00001.parquet
  - split: NanoFiQA2018
    path: corpus/NanoFiQA2018-00000-of-00001.parquet
  - split: NanoHotpotQA
    path: corpus/NanoHotpotQA-00000-of-00001.parquet
  - split: NanoMSMARCO
    path: corpus/NanoMSMARCO-00000-of-00001.parquet
  - split: NanoNFCorpus
    path: corpus/NanoNFCorpus-00000-of-00001.parquet
  - split: NanoNQ
    path: corpus/NanoNQ-00000-of-00001.parquet
  - split: NanoQuoraRetrieval
    path: corpus/NanoQuoraRetrieval-00000-of-00001.parquet
  - split: NanoSCIDOCS
    path: corpus/NanoSCIDOCS-00000-of-00001.parquet
  - split: NanoSciFact
    path: corpus/NanoSciFact-00000-of-00001.parquet
  - split: NanoTouche2020
    path: corpus/NanoTouche2020-00000-of-00001.parquet
- config_name: queries
  data_files:
  - split: NanoArguAna
    path: queries/NanoArguAna-00000-of-00001.parquet
  - split: NanoClimateFEVER
    path: queries/NanoClimateFEVER-00000-of-00001.parquet
  - split: NanoDBPedia
    path: queries/NanoDBPedia-00000-of-00001.parquet
  - split: NanoFEVER
    path: queries/NanoFEVER-00000-of-00001.parquet
  - split: NanoFiQA2018
    path: queries/NanoFiQA2018-00000-of-00001.parquet
  - split: NanoHotpotQA
    path: queries/NanoHotpotQA-00000-of-00001.parquet
  - split: NanoMSMARCO
    path: queries/NanoMSMARCO-00000-of-00001.parquet
  - split: NanoNFCorpus
    path: queries/NanoNFCorpus-00000-of-00001.parquet
  - split: NanoNQ
    path: queries/NanoNQ-00000-of-00001.parquet
  - split: NanoQuoraRetrieval
    path: queries/NanoQuoraRetrieval-00000-of-00001.parquet
  - split: NanoSCIDOCS
    path: queries/NanoSCIDOCS-00000-of-00001.parquet
  - split: NanoSciFact
    path: queries/NanoSciFact-00000-of-00001.parquet
  - split: NanoTouche2020
    path: queries/NanoTouche2020-00000-of-00001.parquet
  default: true
- config_name: qrels
  data_files:
  - split: NanoArguAna
    path: qrels/NanoArguAna-00000-of-00001.parquet
  - split: NanoClimateFEVER
    path: qrels/NanoClimateFEVER-00000-of-00001.parquet
  - split: NanoDBPedia
    path: qrels/NanoDBPedia-00000-of-00001.parquet
  - split: NanoFEVER
    path: qrels/NanoFEVER-00000-of-00001.parquet
  - split: NanoFiQA2018
    path: qrels/NanoFiQA2018-00000-of-00001.parquet
  - split: NanoHotpotQA
    path: qrels/NanoHotpotQA-00000-of-00001.parquet
  - split: NanoMSMARCO
    path: qrels/NanoMSMARCO-00000-of-00001.parquet
  - split: NanoNFCorpus
    path: qrels/NanoNFCorpus-00000-of-00001.parquet
  - split: NanoNQ
    path: qrels/NanoNQ-00000-of-00001.parquet
  - split: NanoQuoraRetrieval
    path: qrels/NanoQuoraRetrieval-00000-of-00001.parquet
  - split: NanoSCIDOCS
    path: qrels/NanoSCIDOCS-00000-of-00001.parquet
  - split: NanoSciFact
    path: qrels/NanoSciFact-00000-of-00001.parquet
  - split: NanoTouche2020
    path: qrels/NanoTouche2020-00000-of-00001.parquet
- config_name: bm25
  data_files:
  - split: NanoArguAna
    path: bm25/NanoArguAna-00000-of-00001.parquet
  - split: NanoClimateFEVER
    path: bm25/NanoClimateFEVER-00000-of-00001.parquet
  - split: NanoDBPedia
    path: bm25/NanoDBPedia-00000-of-00001.parquet
  - split: NanoFEVER
    path: bm25/NanoFEVER-00000-of-00001.parquet
  - split: NanoFiQA2018
    path: bm25/NanoFiQA2018-00000-of-00001.parquet
  - split: NanoHotpotQA
    path: bm25/NanoHotpotQA-00000-of-00001.parquet
  - split: NanoMSMARCO
    path: bm25/NanoMSMARCO-00000-of-00001.parquet
  - split: NanoNFCorpus
    path: bm25/NanoNFCorpus-00000-of-00001.parquet
  - split: NanoNQ
    path: bm25/NanoNQ-00000-of-00001.parquet
  - split: NanoQuoraRetrieval
    path: bm25/NanoQuoraRetrieval-00000-of-00001.parquet
  - split: NanoSCIDOCS
    path: bm25/NanoSCIDOCS-00000-of-00001.parquet
  - split: NanoSciFact
    path: bm25/NanoSciFact-00000-of-00001.parquet
  - split: NanoTouche2020
    path: bm25/NanoTouche2020-00000-of-00001.parquet
- config_name: harrier_oss_v1_270m
  data_files:
  - split: NanoArguAna
    path: harrier_oss_v1_270m/NanoArguAna-00000-of-00001.parquet
  - split: NanoClimateFEVER
    path: harrier_oss_v1_270m/NanoClimateFEVER-00000-of-00001.parquet
  - split: NanoDBPedia
    path: harrier_oss_v1_270m/NanoDBPedia-00000-of-00001.parquet
  - split: NanoFEVER
    path: harrier_oss_v1_270m/NanoFEVER-00000-of-00001.parquet
  - split: NanoFiQA2018
    path: harrier_oss_v1_270m/NanoFiQA2018-00000-of-00001.parquet
  - split: NanoHotpotQA
    path: harrier_oss_v1_270m/NanoHotpotQA-00000-of-00001.parquet
  - split: NanoMSMARCO
    path: harrier_oss_v1_270m/NanoMSMARCO-00000-of-00001.parquet
  - split: NanoNFCorpus
    path: harrier_oss_v1_270m/NanoNFCorpus-00000-of-00001.parquet
  - split: NanoNQ
    path: harrier_oss_v1_270m/NanoNQ-00000-of-00001.parquet
  - split: NanoQuoraRetrieval
    path: harrier_oss_v1_270m/NanoQuoraRetrieval-00000-of-00001.parquet
  - split: NanoSCIDOCS
    path: harrier_oss_v1_270m/NanoSCIDOCS-00000-of-00001.parquet
  - split: NanoSciFact
    path: harrier_oss_v1_270m/NanoSciFact-00000-of-00001.parquet
  - split: NanoTouche2020
    path: harrier_oss_v1_270m/NanoTouche2020-00000-of-00001.parquet
- config_name: reranking_hybrid
  data_files:
  - split: NanoArguAna
    path: reranking_hybrid/NanoArguAna-00000-of-00001.parquet
  - split: NanoClimateFEVER
    path: reranking_hybrid/NanoClimateFEVER-00000-of-00001.parquet
  - split: NanoDBPedia
    path: reranking_hybrid/NanoDBPedia-00000-of-00001.parquet
  - split: NanoFEVER
    path: reranking_hybrid/NanoFEVER-00000-of-00001.parquet
  - split: NanoFiQA2018
    path: reranking_hybrid/NanoFiQA2018-00000-of-00001.parquet
  - split: NanoHotpotQA
    path: reranking_hybrid/NanoHotpotQA-00000-of-00001.parquet
  - split: NanoMSMARCO
    path: reranking_hybrid/NanoMSMARCO-00000-of-00001.parquet
  - split: NanoNFCorpus
    path: reranking_hybrid/NanoNFCorpus-00000-of-00001.parquet
  - split: NanoNQ
    path: reranking_hybrid/NanoNQ-00000-of-00001.parquet
  - split: NanoQuoraRetrieval
    path: reranking_hybrid/NanoQuoraRetrieval-00000-of-00001.parquet
  - split: NanoSCIDOCS
    path: reranking_hybrid/NanoSCIDOCS-00000-of-00001.parquet
  - split: NanoSciFact
    path: reranking_hybrid/NanoSciFact-00000-of-00001.parquet
  - split: NanoTouche2020
    path: reranking_hybrid/NanoTouche2020-00000-of-00001.parquet
language:
- en
tags:
- information-retrieval
- retrieval
- nano
- bm25
- hakari-bench
- dense-retrieval
- reranking
dataset_info:
- config_name: bm25
  features:
  - name: query-id
    dtype: string
  - name: corpus-ids
    list: string
  splits:
  - name: NanoArguAna
    num_bytes: 968728
    num_examples: 50
  - name: NanoClimateFEVER
    num_bytes: 600802
    num_examples: 50
  - name: NanoDBPedia
    num_bytes: 874981
    num_examples: 50
  - name: NanoFEVER
    num_bytes: 593474
    num_examples: 50
  - name: NanoFiQA2018
    num_bytes: 245847
    num_examples: 50
  - name: NanoHotpotQA
    num_bytes: 289577
    num_examples: 50
  - name: NanoMSMARCO
    num_bytes: 274039
    num_examples: 50
  - name: NanoNFCorpus
    num_bytes: 300134
    num_examples: 50
  - name: NanoNQ
    num_bytes: 334792
    num_examples: 50
  - name: NanoQuoraRetrieval
    num_bytes: 245563
    num_examples: 50
  - name: NanoSCIDOCS
    num_bytes: 1102400
    num_examples: 50
  - name: NanoSciFact
    num_bytes: 290615
    num_examples: 50
  - name: NanoTouche2020
    num_bytes: 1052535
    num_examples: 49
  download_size: 7191734
  dataset_size: 7173508
- config_name: corpus
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: NanoArguAna
    num_bytes: 3854860
    num_examples: 3635
  - name: NanoClimateFEVER
    num_bytes: 5617105
    num_examples: 3408
  - name: NanoDBPedia
    num_bytes: 2280448
    num_examples: 6045
  - name: NanoFEVER
    num_bytes: 6285894
    num_examples: 4996
  - name: NanoFiQA2018
    num_bytes: 4201493
    num_examples: 4598
  - name: NanoHotpotQA
    num_bytes: 1868234
    num_examples: 5090
  - name: NanoMSMARCO
    num_bytes: 1745074
    num_examples: 5043
  - name: NanoNFCorpus
    num_bytes: 4521394
    num_examples: 2953
  - name: NanoNQ
    num_bytes: 2740852
    num_examples: 5035
  - name: NanoQuoraRetrieval
    num_bytes: 346228
    num_examples: 5046
  - name: NanoSCIDOCS
    num_bytes: 2149671
    num_examples: 2210
  - name: NanoSciFact
    num_bytes: 4227131
    num_examples: 2919
  - name: NanoTouche2020
    num_bytes: 12592148
    num_examples: 5745
  download_size: 30724168
  dataset_size: 52430532
- config_name: harrier_oss_v1_270m
  features:
  - name: query-id
    dtype: string
  - name: corpus-ids
    list: string
  splits:
  - name: NanoArguAna
    num_bytes: 969492
    num_examples: 50
  - name: NanoClimateFEVER
    num_bytes: 669504
    num_examples: 50
  - name: NanoDBPedia
    num_bytes: 892493
    num_examples: 50
  - name: NanoFEVER
    num_bytes: 596334
    num_examples: 50
  - name: NanoFiQA2018
    num_bytes: 245652
    num_examples: 50
  - name: NanoHotpotQA
    num_bytes: 289712
    num_examples: 50
  - name: NanoMSMARCO
    num_bytes: 272519
    num_examples: 50
  - name: NanoNFCorpus
    num_bytes: 299748
    num_examples: 50
  - name: NanoNQ
    num_bytes: 336747
    num_examples: 50
  - name: NanoQuoraRetrieval
    num_bytes: 244373
    num_examples: 50
  - name: NanoSCIDOCS
    num_bytes: 1102400
    num_examples: 50
  - name: NanoSciFact
    num_bytes: 291249
    num_examples: 50
  - name: NanoTouche2020
    num_bytes: 1052515
    num_examples: 49
  download_size: 7281112
  dataset_size: 7262738
- config_name: qrels
  features:
  - name: query-id
    dtype: string
  - name: corpus-id
    dtype: string
  splits:
  - name: NanoArguAna
    num_bytes: 3496
    num_examples: 50
  - name: NanoClimateFEVER
    num_bytes: 4361
    num_examples: 148
  - name: NanoDBPedia
    num_bytes: 60640
    num_examples: 1158
  - name: NanoFEVER
    num_bytes: 1630
    num_examples: 57
  - name: NanoFiQA2018
    num_bytes: 2200
    num_examples: 123
  - name: NanoHotpotQA
    num_bytes: 3885
    num_examples: 100
  - name: NanoMSMARCO
    num_bytes: 1065
    num_examples: 50
  - name: NanoNFCorpus
    num_bytes: 64851
    num_examples: 2518
  - name: NanoNQ
    num_bytes: 1340
    num_examples: 57
  - name: NanoQuoraRetrieval
    num_bytes: 1359
    num_examples: 70
  - name: NanoSCIDOCS
    num_bytes: 21472
    num_examples: 244
  - name: NanoSciFact
    num_bytes: 1054
    num_examples: 56
  - name: NanoTouche2020
    num_bytes: 45452
    num_examples: 932
  download_size: 88208
  dataset_size: 190263
- config_name: queries
  features:
  - name: _id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: NanoArguAna
    num_bytes: 62331
    num_examples: 50
  - name: NanoClimateFEVER
    num_bytes: 7044
    num_examples: 50
  - name: NanoDBPedia
    num_bytes: 2723
    num_examples: 50
  - name: NanoFEVER
    num_bytes: 2948
    num_examples: 50
  - name: NanoFiQA2018
    num_bytes: 3531
    num_examples: 50
  - name: NanoHotpotQA
    num_bytes: 6019
    num_examples: 50
  - name: NanoMSMARCO
    num_bytes: 2328
    num_examples: 50
  - name: NanoNFCorpus
    num_bytes: 1939
    num_examples: 50
  - name: NanoNQ
    num_bytes: 3132
    num_examples: 50
  - name: NanoQuoraRetrieval
    num_bytes: 3087
    num_examples: 50
  - name: NanoSCIDOCS
    num_bytes: 6041
    num_examples: 50
  - name: NanoSciFact
    num_bytes: 5352
    num_examples: 50
  - name: NanoTouche2020
    num_bytes: 2609
    num_examples: 49
  download_size: 97288
  dataset_size: 109084
- config_name: reranking_hybrid
  features:
  - name: query-id
    dtype: string
  - name: corpus-ids
    list: string
  splits:
  - name: NanoArguAna
    num_bytes: 193478
    num_examples: 50
  - name: NanoClimateFEVER
    num_bytes: 136455
    num_examples: 50
  - name: NanoDBPedia
    num_bytes: 180273
    num_examples: 50
  - name: NanoFEVER
    num_bytes: 120469
    num_examples: 50
  - name: NanoFiQA2018
    num_bytes: 49621
    num_examples: 50
  - name: NanoHotpotQA
    num_bytes: 59078
    num_examples: 50
  - name: NanoMSMARCO
    num_bytes: 55157
    num_examples: 50
  - name: NanoNFCorpus
    num_bytes: 60573
    num_examples: 50
  - name: NanoNQ
    num_bytes: 67868
    num_examples: 50
  - name: NanoQuoraRetrieval
    num_bytes: 49452
    num_examples: 50
  - name: NanoSCIDOCS
    num_bytes: 222444
    num_examples: 50
  - name: NanoSciFact
    num_bytes: 58623
    num_examples: 50
  - name: NanoTouche2020
    num_bytes: 210905
    num_examples: 49
  download_size: 1482130
  dataset_size: 1464448
---
# NanoBEIR-en

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

NanoBEIR-en is a compact English benchmark derived from BEIR retrieval datasets. It keeps the query-corpus-qrels retrieval format while using small task splits for fast, repeatable evaluation.

## Usage

```python
from datasets import load_dataset

dataset_id = "hakari-bench/NanoBEIR-en"
split = "NanoArguAna"

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. `NanoNFCorpus` includes the full positive qrels (2,518 rows); qrels are not capped to the top-100 reranking depth.

## Candidate Construction

- `bm25`: local BM25 top-500 with automatic tokenizer selection. Auto mode uses `wordseg` for `ja`, `zh`, `th`, `ko`, and `vi`, and `regex` otherwise. The resolved tokenizer is shown for each split in the Candidate Quality table.
- `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. Qrels are not capped to fit the top-100 reranking depth. For `NanoNFCorpus`, some queries have more than 100 positive qrels, so top-100 hybrid candidate coverage is expected to be below 100%; this is a candidate-list diagnostic, not a qrels filtering rule.

## 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 |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| NanoArguAna | 50 | 3635 | 50 | 1201.8 | 1170.5 | 1446.5 | 1011.8 | 904.0 | 1259.0 |
| NanoClimateFEVER | 50 | 3408 | 148 | 128.4 | 124.0 | 158.0 | 1619.5 | 1461.0 | 2079.8 |
| NanoDBPedia | 50 | 6045 | 1158 | 33.1 | 33.5 | 45.5 | 336.3 | 369.0 | 445.0 |
| NanoFEVER | 50 | 4996 | 57 | 45.4 | 43.0 | 53.0 | 1228.7 | 1041.0 | 1649.0 |
| NanoFiQA2018 | 50 | 4598 | 123 | 58.5 | 55.0 | 75.2 | 899.6 | 651.0 | 1136.8 |
| NanoHotpotQA | 50 | 5090 | 100 | 88.3 | 82.5 | 107.0 | 349.6 | 299.0 | 479.0 |
| NanoMSMARCO | 50 | 5043 | 50 | 32.2 | 29.0 | 40.0 | 330.2 | 298.0 | 380.0 |
| NanoNFCorpus | 50 | 2953 | 2518 | 21.0 | 16.5 | 31.5 | 1512.7 | 1532.0 | 1775.0 |
| NanoNQ | 50 | 5035 | 57 | 47.0 | 42.5 | 53.0 | 525.6 | 443.0 | 758.0 |
| NanoQuoraRetrieval | 50 | 5046 | 70 | 48.0 | 43.5 | 54.8 | 54.8 | 47.0 | 64.0 |
| NanoSCIDOCS | 50 | 2210 | 244 | 72.8 | 71.5 | 81.8 | 923.6 | 910.5 | 1230.5 |
| NanoSciFact | 50 | 2919 | 56 | 95.8 | 92.5 | 124.8 | 1431.2 | 1344.0 | 1725.0 |
| NanoTouche2020 | 49 | 5745 | 932 | 43.4 | 40.0 | 57.0 | 2142.6 | 989.0 | 3032.0 |

## 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 | - | 57.34 | 61.06 | 61.80 | 81.58 | 84.35 | 86.43 | - | 13 |
| NanoArguAna | english_porter_stop | 46.50 | 57.87 | 54.22 | 100.00 | 94.00 | 100.00 | 100 | 0 |
| NanoClimateFEVER | english_porter_stop | 32.66 | 28.11 | 34.19 | 60.50 | 72.83 | 74.33 | 100-101 | 1 |
| NanoDBPedia | english_porter_stop | 63.74 | 62.43 | 65.64 | 77.87 | 79.94 | 85.56 | 100 | 0 |
| NanoFEVER | english_porter_stop | 81.43 | 88.16 | 85.21 | 100.00 | 98.00 | 100.00 | 100 | 0 |
| NanoFiQA2018 | english_porter_stop | 42.11 | 50.11 | 51.50 | 73.51 | 77.31 | 81.59 | 100-101 | 4 |
| NanoHotpotQA | english_porter_stop | 82.70 | 80.43 | 83.25 | 96.00 | 91.00 | 97.00 | 100 | 0 |
| NanoMSMARCO | english_porter_stop | 52.17 | 61.88 | 61.70 | 100.00 | 100.00 | 100.00 | 100 | 0 |
| NanoNFCorpus | regex@regex | 33.03 | 33.81 | 35.32 | 20.22 | 30.44 | 31.69 | 100-101 | 5 |
| NanoNQ | english_porter_stop | 51.40 | 67.26 | 65.84 | 92.00 | 100.00 | 97.00 | 100-101 | 1 |
| NanoQuoraRetrieval | english_porter_stop | 87.45 | 88.88 | 91.05 | 100.00 | 96.00 | 100.00 | 100 | 0 |
| NanoSCIDOCS | english_porter_stop | 32.94 | 43.92 | 39.62 | 61.37 | 81.40 | 71.20 | 100-101 | 1 |
| NanoSciFact | english_porter_stop | 72.82 | 76.79 | 73.97 | 94.00 | 92.00 | 98.00 | 100-101 | 1 |
| NanoTouche2020 | english_porter_stop | 66.48 | 54.07 | 61.84 | 85.11 | 83.65 | 87.18 | 100 | 0 |

## Hybrid Safeguard Summary

- Safeguard positives: 13
- Rows limited by corpus size: 0
- Metadata file: `reranking_hybrid_metadata.json`

## Source Links

- Original dataset: [sentence-transformers/NanoBEIR-en](https://huggingface.co/datasets/sentence-transformers/NanoBEIR-en)
- Final dataset: [hakari-bench/NanoBEIR-en](https://huggingface.co/datasets/hakari-bench/NanoBEIR-en)

## License

NanoBEIR-en is a derived dataset. Users must comply with the licenses,
terms, and attribution requirements of the upstream source datasets.