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NanoBEIR-th

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metadata 
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:
  - config_name: bm25
    features:
      - name: query-id
        dtype: string
      - name: corpus-ids
        list: string
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      - name: NanoArguAna
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        num_examples: 50
      - name: NanoClimateFEVER
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        num_examples: 50
      - name: NanoDBPedia
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        num_examples: 50
      - name: NanoFEVER
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        num_examples: 50
      - name: NanoFiQA2018
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        num_examples: 50
      - name: NanoHotpotQA
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        num_examples: 50
      - name: NanoMSMARCO
        num_bytes: 273112
        num_examples: 50
      - name: NanoNFCorpus
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        num_examples: 50
      - name: NanoNQ
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        num_examples: 50
      - name: NanoQuoraRetrieval
        num_bytes: 244341
        num_examples: 50
      - name: NanoSCIDOCS
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        num_examples: 50
      - name: NanoSciFact
        num_bytes: 290835
        num_examples: 50
      - name: NanoTouche2020
        num_bytes: 1052551
        num_examples: 49
    download_size: 7181981
    dataset_size: 7163682
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
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      - name: NanoArguAna
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        num_examples: 3635
      - name: NanoClimateFEVER
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      - name: NanoDBPedia
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      - name: NanoFEVER
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        num_examples: 4996
      - name: NanoFiQA2018
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      - name: NanoHotpotQA
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        num_examples: 5090
      - name: NanoMSMARCO
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        num_examples: 5043
      - name: NanoNFCorpus
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        num_examples: 2953
      - name: NanoNQ
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        num_examples: 5035
      - name: NanoQuoraRetrieval
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        num_examples: 5046
      - name: NanoSCIDOCS
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      - name: NanoSciFact
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        num_examples: 2919
      - name: NanoTouche2020
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        num_examples: 5745
    download_size: 44434242
    dataset_size: 120414519
  - config_name: harrier_oss_v1_270m
    features:
      - name: query-id
        dtype: string
      - name: corpus-ids
        list: string
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      - name: NanoArguAna
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        num_examples: 50
      - name: NanoClimateFEVER
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        num_examples: 50
      - name: NanoDBPedia
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      - name: NanoFEVER
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        num_examples: 50
      - name: NanoFiQA2018
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      - name: NanoHotpotQA
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        num_examples: 50
      - name: NanoMSMARCO
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      - name: NanoNFCorpus
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        num_examples: 50
      - name: NanoNQ
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        num_examples: 50
      - name: NanoQuoraRetrieval
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        num_examples: 50
      - name: NanoSCIDOCS
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        num_examples: 50
      - name: NanoSciFact
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        num_examples: 50
      - name: NanoTouche2020
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    download_size: 7241287
    dataset_size: 7222829
  - config_name: qrels
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      - name: query-id
        dtype: string
      - name: corpus-id
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      - name: NanoArguAna
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      - name: NanoClimateFEVER
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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: 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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    download_size: 88208
    dataset_size: 190263
  - config_name: queries
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      - name: text
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      - name: NanoClimateFEVER
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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: reranking_hybrid
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      - name: query-id
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      - name: corpus-ids
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      - name: NanoClimateFEVER
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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: NanoNQ
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        num_examples: 50
      - name: NanoQuoraRetrieval
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        num_examples: 50
      - name: NanoSCIDOCS
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        num_examples: 50
      - name: NanoSciFact
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        num_examples: 50
      - name: NanoTouche2020
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    download_size: 1477385
    dataset_size: 1459764

NanoBEIR-th

This dataset is a Nano-style retrieval dataset for 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 

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

License

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