Datasets:

hakari-bench
/

NanoIFIR

configs:
  - config_name: corpus
    data_files:
      - split: NanoIFIRAila
        path: corpus/NanoIFIRAila-00000-of-00001.parquet
      - split: NanoIFIRCds
        path: corpus/NanoIFIRCds-00000-of-00001.parquet
      - split: NanoIFIRFiQA
        path: corpus/NanoIFIRFiQA-00000-of-00001.parquet
      - split: NanoIFIRFire
        path: corpus/NanoIFIRFire-00000-of-00001.parquet
      - split: NanoIFIRNFCorpus
        path: corpus/NanoIFIRNFCorpus-00000-of-00001.parquet
      - split: NanoIFIRPm
        path: corpus/NanoIFIRPm-00000-of-00001.parquet
      - split: NanoIFIRScifact
        path: corpus/NanoIFIRScifact-00000-of-00001.parquet
  - config_name: queries
    data_files:
      - split: NanoIFIRAila
        path: queries/NanoIFIRAila-00000-of-00001.parquet
      - split: NanoIFIRCds
        path: queries/NanoIFIRCds-00000-of-00001.parquet
      - split: NanoIFIRFiQA
        path: queries/NanoIFIRFiQA-00000-of-00001.parquet
      - split: NanoIFIRFire
        path: queries/NanoIFIRFire-00000-of-00001.parquet
      - split: NanoIFIRNFCorpus
        path: queries/NanoIFIRNFCorpus-00000-of-00001.parquet
      - split: NanoIFIRPm
        path: queries/NanoIFIRPm-00000-of-00001.parquet
      - split: NanoIFIRScifact
        path: queries/NanoIFIRScifact-00000-of-00001.parquet
    default: true
  - config_name: qrels
    data_files:
      - split: NanoIFIRAila
        path: qrels/NanoIFIRAila-00000-of-00001.parquet
      - split: NanoIFIRCds
        path: qrels/NanoIFIRCds-00000-of-00001.parquet
      - split: NanoIFIRFiQA
        path: qrels/NanoIFIRFiQA-00000-of-00001.parquet
      - split: NanoIFIRFire
        path: qrels/NanoIFIRFire-00000-of-00001.parquet
      - split: NanoIFIRNFCorpus
        path: qrels/NanoIFIRNFCorpus-00000-of-00001.parquet
      - split: NanoIFIRPm
        path: qrels/NanoIFIRPm-00000-of-00001.parquet
      - split: NanoIFIRScifact
        path: qrels/NanoIFIRScifact-00000-of-00001.parquet
  - config_name: bm25
    data_files:
      - split: NanoIFIRAila
        path: bm25/NanoIFIRAila-00000-of-00001.parquet
      - split: NanoIFIRCds
        path: bm25/NanoIFIRCds-00000-of-00001.parquet
      - split: NanoIFIRFiQA
        path: bm25/NanoIFIRFiQA-00000-of-00001.parquet
      - split: NanoIFIRFire
        path: bm25/NanoIFIRFire-00000-of-00001.parquet
      - split: NanoIFIRNFCorpus
        path: bm25/NanoIFIRNFCorpus-00000-of-00001.parquet
      - split: NanoIFIRPm
        path: bm25/NanoIFIRPm-00000-of-00001.parquet
      - split: NanoIFIRScifact
        path: bm25/NanoIFIRScifact-00000-of-00001.parquet
  - config_name: harrier_oss_v1_270m
    data_files:
      - split: NanoIFIRAila
        path: harrier_oss_v1_270m/NanoIFIRAila-00000-of-00001.parquet
      - split: NanoIFIRCds
        path: harrier_oss_v1_270m/NanoIFIRCds-00000-of-00001.parquet
      - split: NanoIFIRFiQA
        path: harrier_oss_v1_270m/NanoIFIRFiQA-00000-of-00001.parquet
      - split: NanoIFIRFire
        path: harrier_oss_v1_270m/NanoIFIRFire-00000-of-00001.parquet
      - split: NanoIFIRNFCorpus
        path: harrier_oss_v1_270m/NanoIFIRNFCorpus-00000-of-00001.parquet
      - split: NanoIFIRPm
        path: harrier_oss_v1_270m/NanoIFIRPm-00000-of-00001.parquet
      - split: NanoIFIRScifact
        path: harrier_oss_v1_270m/NanoIFIRScifact-00000-of-00001.parquet
  - config_name: reranking_hybrid
    data_files:
      - split: NanoIFIRAila
        path: reranking_hybrid/NanoIFIRAila-00000-of-00001.parquet
      - split: NanoIFIRCds
        path: reranking_hybrid/NanoIFIRCds-00000-of-00001.parquet
      - split: NanoIFIRFiQA
        path: reranking_hybrid/NanoIFIRFiQA-00000-of-00001.parquet
      - split: NanoIFIRFire
        path: reranking_hybrid/NanoIFIRFire-00000-of-00001.parquet
      - split: NanoIFIRNFCorpus
        path: reranking_hybrid/NanoIFIRNFCorpus-00000-of-00001.parquet
      - split: NanoIFIRPm
        path: reranking_hybrid/NanoIFIRPm-00000-of-00001.parquet
      - split: NanoIFIRScifact
        path: reranking_hybrid/NanoIFIRScifact-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: NanoIFIRAila
        num_bytes: 173262
        num_examples: 40
      - name: NanoIFIRCds
        num_bytes: 231700
        num_examples: 42
      - name: NanoIFIRFiQA
        num_bytes: 907601
        num_examples: 200
      - name: NanoIFIRFire
        num_bytes: 670505
        num_examples: 167
      - name: NanoIFIRNFCorpus
        num_bytes: 515648
        num_examples: 86
      - name: NanoIFIRPm
        num_bytes: 443554
        num_examples: 59
      - name: NanoIFIRScifact
        num_bytes: 213810
        num_examples: 43
    download_size: 3163052
    dataset_size: 3156080
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: NanoIFIRAila
        num_bytes: 58311115
        num_examples: 2914
      - name: NanoIFIRCds
        num_bytes: 16485735
        num_examples: 10000
      - name: NanoIFIRFiQA
        num_bytes: 8056651
        num_examples: 10000
      - name: NanoIFIRFire
        num_bytes: 47265438
        num_examples: 1739
      - name: NanoIFIRNFCorpus
        num_bytes: 5776745
        num_examples: 3593
      - name: NanoIFIRPm
        num_bytes: 22638705
        num_examples: 10000
      - name: NanoIFIRScifact
        num_bytes: 14684875
        num_examples: 10000
    download_size: 88122092
    dataset_size: 173219264
  - config_name: harrier_oss_v1_270m
    features:
      - name: query-id
        dtype: string
      - name: corpus-ids
        list: string
    splits:
      - name: NanoIFIRAila
        num_bytes: 173683
        num_examples: 40
      - name: NanoIFIRCds
        num_bytes: 231689
        num_examples: 42
      - name: NanoIFIRFiQA
        num_bytes: 910347
        num_examples: 200
      - name: NanoIFIRFire
        num_bytes: 670505
        num_examples: 167
      - name: NanoIFIRNFCorpus
        num_bytes: 515404
        num_examples: 86
      - name: NanoIFIRPm
        num_bytes: 443554
        num_examples: 59
      - name: NanoIFIRScifact
        num_bytes: 213773
        num_examples: 43
    download_size: 3165784
    dataset_size: 3158955
  - config_name: qrels
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
    splits:
      - name: NanoIFIRAila
        num_bytes: 2653
        num_examples: 119
      - name: NanoIFIRCds
        num_bytes: 11477
        num_examples: 466
      - name: NanoIFIRFiQA
        num_bytes: 20948
        num_examples: 1010
      - name: NanoIFIRFire
        num_bytes: 10697
        num_examples: 563
      - name: NanoIFIRNFCorpus
        num_bytes: 6883
        num_examples: 242
      - name: NanoIFIRPm
        num_bytes: 35074
        num_examples: 1217
      - name: NanoIFIRScifact
        num_bytes: 5581
        num_examples: 255
    download_size: 41077
    dataset_size: 93313
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: NanoIFIRAila
        num_bytes: 116355
        num_examples: 40
      - name: NanoIFIRCds
        num_bytes: 10203
        num_examples: 42
      - name: NanoIFIRFiQA
        num_bytes: 16200
        num_examples: 200
      - name: NanoIFIRFire
        num_bytes: 550908
        num_examples: 167
      - name: NanoIFIRNFCorpus
        num_bytes: 5031
        num_examples: 86
      - name: NanoIFIRPm
        num_bytes: 9653
        num_examples: 59
      - name: NanoIFIRScifact
        num_bytes: 3777
        num_examples: 43
    download_size: 365390
    dataset_size: 712127
  - config_name: reranking_hybrid
    features:
      - name: query-id
        dtype: string
      - name: corpus-ids
        list: string
    splits:
      - name: NanoIFIRAila
        num_bytes: 35559
        num_examples: 40
      - name: NanoIFIRCds
        num_bytes: 46968
        num_examples: 42
      - name: NanoIFIRFiQA
        num_bytes: 185023
        num_examples: 200
      - name: NanoIFIRFire
        num_bytes: 136201
        num_examples: 167
      - name: NanoIFIRNFCorpus
        num_bytes: 104647
        num_examples: 86
      - name: NanoIFIRPm
        num_bytes: 89569
        num_examples: 59
      - name: NanoIFIRScifact
        num_bytes: 43800
        num_examples: 43
    download_size: 648423
    dataset_size: 641767

NanoIFIR

This dataset is a Nano-style retrieval dataset for HAKARI-bench.

NanoIFIR contains 7 Nano retrieval splits derived from IFIR. 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

from datasets import load_dataset

dataset_id = "hakari-bench/NanoIFIR"
split = "NanoIFIRAila"

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
NanoIFIRAila	40	2914	119	2890.1	3012.0	3581.5	19998.1	15303.0	24733.2
NanoIFIRCds	42	10000	466	225.2	213.0	260.2	1630.2	1604.0	1983.0
NanoIFIRFiQA	200	10000	1010	65.8	64.5	78.2	791.9	541.0	975.0
NanoIFIRFire	167	1739	563	3283.8	3242.0	3574.5	27167.7	24688.0	36394.5
NanoIFIRNFCorpus	86	3593	242	37.8	37.0	44.8	1589.5	1612.0	1868.0
NanoIFIRPm	59	10000	1217	145.7	146.0	157.0	2244.9	1651.5	2792.5
NanoIFIRScifact	43	10000	255	73.6	73.0	90.5	1452.6	1454.0	1819.0

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	-	37.84	46.06	44.97	59.74	72.00	72.71	-	49
NanoIFIRAila	english_porter_stop	9.88	8.78	7.98	28.30	35.19	35.35	100-101	21
NanoIFIRCds	english_porter_stop	22.58	40.73	33.76	35.88	72.48	67.06	100-101	3
NanoIFIRFiQA	english_porter_stop	34.22	53.28	46.78	62.14	79.86	77.92	100-101	3
NanoIFIRFire	english_porter_stop	35.66	34.21	39.96	74.36	69.22	76.63	100-101	12
NanoIFIRNFCorpus	english_porter_stop	33.38	45.80	41.08	63.58	83.40	78.27	100-101	9
NanoIFIRPm	english_porter_stop	42.32	54.48	54.68	55.85	68.25	74.18	100-101	1
NanoIFIRScifact	english_porter_stop	86.82	85.16	90.55	98.10	95.56	99.57	100	0

Hybrid Safeguard Summary

Safeguard positives: 49
Rows limited by corpus size: 0
Metadata file: reranking_hybrid_metadata.json

Source Links

Source benchmark: IFIR
if-ir/aila: https://huggingface.co/datasets/if-ir/aila
if-ir/cds: https://huggingface.co/datasets/if-ir/cds
if-ir/fiqa: https://huggingface.co/datasets/if-ir/fiqa
if-ir/fire: https://huggingface.co/datasets/if-ir/fire
if-ir/nfcorpus: https://huggingface.co/datasets/if-ir/nfcorpus
if-ir/pm: https://huggingface.co/datasets/if-ir/pm
if-ir/scifact_open: https://huggingface.co/datasets/if-ir/scifact_open

License

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