Datasets:
File size: 18,820 Bytes
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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:
- multilingual
tags:
- information-retrieval
- retrieval
- nano
- bm25
- hakari-bench
- dense-retrieval
- reranking
dataset_info:
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features:
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dtype: string
- name: corpus-ids
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- name: NanoDBPedia
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- name: NanoFEVER
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- name: NanoFiQA2018
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- name: NanoHotpotQA
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- name: NanoMSMARCO
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- name: NanoNFCorpus
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- name: NanoNQ
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- name: NanoQuoraRetrieval
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- name: NanoSCIDOCS
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- name: NanoSciFact
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- name: NanoTouche2020
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- config_name: corpus
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- name: NanoSciFact
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- config_name: harrier_oss_v1_270m
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- name: corpus-ids
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- name: NanoTouche2020
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---
# NanoBEIR-th
This dataset is a Nano-style retrieval dataset for [HAKARI-bench](https://github.com/hakari-bench/hakari-bench).
NanoBEIR-th is the Thai language-specific component of MNanoBEIR. It groups compact BEIR-derived retrieval tasks for efficient evaluation of document ranking in that language.
## Usage
```python
from datasets import load_dataset
dataset_id = "hakari-bench/NanoBEIR-th"
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 | 820.6 | 881.5 | 913.0 | 860.1 | 771.0 | 1073.0 |
| NanoClimateFEVER | 50 | 3408 | 148 | 118.6 | 113.0 | 151.2 | 1395.4 | 1263.5 | 1799.2 |
| NanoDBPedia | 50 | 6045 | 1158 | 30.9 | 28.5 | 42.5 | 316.4 | 345.0 | 414.0 |
| NanoFEVER | 50 | 4996 | 57 | 46.9 | 45.5 | 57.0 | 1084.7 | 930.5 | 1455.2 |
| NanoFiQA2018 | 50 | 4598 | 123 | 55.2 | 51.5 | 70.0 | 779.2 | 574.5 | 999.8 |
| NanoHotpotQA | 50 | 5090 | 100 | 79.7 | 74.0 | 99.8 | 330.7 | 287.0 | 449.0 |
| NanoMSMARCO | 50 | 5043 | 50 | 32.1 | 29.0 | 38.5 | 293.9 | 266.0 | 339.0 |
| NanoNFCorpus | 50 | 2953 | 2518 | 22.6 | 20.5 | 31.0 | 1387.4 | 1409.0 | 1647.0 |
| NanoNQ | 50 | 5035 | 57 | 40.8 | 39.0 | 44.8 | 473.6 | 402.0 | 673.0 |
| NanoQuoraRetrieval | 50 | 5046 | 70 | 46.9 | 42.0 | 56.0 | 53.7 | 47.0 | 62.0 |
| NanoSCIDOCS | 50 | 2210 | 244 | 69.1 | 68.5 | 82.0 | 820.4 | 814.5 | 1100.0 |
| NanoSciFact | 50 | 2919 | 56 | 92.7 | 88.5 | 117.8 | 1328.8 | 1245.0 | 1617.0 |
| NanoTouche2020 | 49 | 5745 | 932 | 46.3 | 42.0 | 55.0 | 1438.1 | 886.0 | 2677.0 |
## 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 | - | 43.71 | 51.02 | 49.61 | 74.11 | 78.86 | 81.39 | - | 31 |
| NanoArguAna | wordseg@th | 40.51 | 37.21 | 43.49 | 94.00 | 90.00 | 92.00 | 100-101 | 4 |
| NanoClimateFEVER | wordseg@th | 23.68 | 34.44 | 30.15 | 58.00 | 69.53 | 71.27 | 100-101 | 2 |
| NanoDBPedia | wordseg@th | 50.43 | 54.68 | 54.82 | 68.43 | 74.92 | 78.09 | 100 | 0 |
| NanoFEVER | wordseg@th | 70.01 | 86.63 | 77.68 | 95.00 | 99.00 | 98.00 | 100-101 | 1 |
| NanoFiQA2018 | wordseg@th | 27.26 | 40.85 | 39.11 | 65.29 | 70.98 | 73.74 | 100-101 | 6 |
| NanoHotpotQA | wordseg@th | 55.23 | 68.80 | 66.52 | 86.00 | 95.00 | 96.00 | 100 | 0 |
| NanoMSMARCO | wordseg@th | 29.07 | 42.65 | 36.53 | 80.00 | 92.00 | 94.00 | 100-101 | 3 |
| NanoNFCorpus | wordseg@th | 26.63 | 24.09 | 27.43 | 18.36 | 21.06 | 25.49 | 100-101 | 6 |
| NanoNQ | wordseg@th | 31.91 | 53.67 | 42.46 | 84.00 | 92.00 | 93.00 | 100-101 | 3 |
| NanoQuoraRetrieval | wordseg@th | 72.67 | 88.59 | 79.28 | 96.00 | 100.00 | 100.00 | 100 | 0 |
| NanoSCIDOCS | wordseg@th | 26.41 | 29.15 | 31.65 | 55.97 | 60.07 | 62.47 | 100-101 | 2 |
| NanoSciFact | wordseg@th | 63.34 | 57.13 | 62.06 | 85.00 | 84.00 | 92.00 | 100-101 | 4 |
| NanoTouche2020 | wordseg@th | 51.08 | 45.34 | 53.80 | 77.38 | 76.65 | 82.07 | 100 | 0 |
## Hybrid Safeguard Summary
- Safeguard positives: 31
- Rows limited by corpus size: 0
- Metadata file: `reranking_hybrid_metadata.json`
## Source Links
- Original dataset: [sionic-ai/NanoBEIR-th](https://huggingface.co/datasets/sionic-ai/NanoBEIR-th)
- Final dataset: [hakari-bench/NanoBEIR-th](https://huggingface.co/datasets/hakari-bench/NanoBEIR-th)
## License
NanoBEIR-th is a derived dataset. Users must comply with the licenses,
terms, and attribution requirements of the upstream source datasets.
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