Hugging Face's logo

Datasets:
hakari-bench
/
NanoBIRCO

Modalities:
Text
Formats:
parquet
Size:
10K - 100K
Tags:
information-retrieval
retrieval
nano
bm25
dense-retrieval
reranking
Libraries:
Datasets
pandas
Polars
Dataset card Data Studio Files
xet
 Community
NanoBIRCO / README.md
hotchpotch's picture
hotchpotch
Update dataset README for reranking_hybrid candidates
2796bcb verified
preview code
|
Raw
History Blame Contribute Delete
10.9 kB
metadata 
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.

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 

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

License

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