Hugging Face's logo

Datasets:
snu-aidas
/
HVMap-dataset

Tasks:
Text Retrieval
Text Ranking
Modalities:
Tabular
Text
Formats:
json
Languages:
English
Size:
100K - 1M
Tags:
hvmap
beir
mteb
text-retrieval
text-ranking
qrels
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
HVMap-dataset / README.md
AIDAS-Lab's picture
AIDAS-Lab
Update README.md
0288b75 verified
preview code
|
Raw
History Blame Contribute Delete
9.82 kB
metadata 
pretty_name: HVMap Dataset
language:
  - en
license: other
task_categories:
  - text-retrieval
  - text-ranking
tags:
  - hvmap
  - beir
  - mteb
  - text-retrieval
  - text-ranking
  - qrels
  - federated-retrieval
  - heterogeneous-vectordb
  - vector-search
  - cross-domain-retrieval
size_categories:
  - 100K<n<1M
configs:
  - config_name: all
    data_files:
      - split: corpus
        path:
          - data/corpus/fiqa.jsonl
          - data/corpus/bioasq.jsonl
          - data/corpus/scidocs.jsonl
          - data/corpus/scifact.jsonl
      - split: queries
        path: data/queries/queries.jsonl
      - split: qrels
        path: data/qrels/qrels.jsonl
  - config_name: corpus
    data_files:
      - split: corpus
        path: data/corpus/*.jsonl
  - config_name: finance
    data_files:
      - split: corpus
        path: data/corpus/fiqa.jsonl
  - config_name: biomedical
    data_files:
      - split: corpus
        path: data/corpus/bioasq.jsonl
  - config_name: science
    data_files:
      - split: corpus
        path:
          - data/corpus/scidocs.jsonl
          - data/corpus/scifact.jsonl
  - config_name: queries
    data_files:
      - split: queries
        path: data/queries/queries.jsonl
  - config_name: qrels
    data_files:
      - split: qrels
        path: data/qrels/qrels.jsonl
  - config_name: fin_bio
    data_files:
      - split: corpus
        path:
          - data/corpus/fiqa.jsonl
          - data/corpus/bioasq.jsonl
      - split: queries
        path: data/queries/fin_bio.jsonl
      - split: qrels
        path: data/qrels/fin_bio.jsonl
  - config_name: bio_sci
    data_files:
      - split: corpus
        path:
          - data/corpus/bioasq.jsonl
          - data/corpus/scidocs.jsonl
          - data/corpus/scifact.jsonl
      - split: queries
        path: data/queries/bio_sci.jsonl
      - split: qrels
        path: data/qrels/bio_sci.jsonl
  - config_name: fin_sci
    data_files:
      - split: corpus
        path:
          - data/corpus/fiqa.jsonl
          - data/corpus/scidocs.jsonl
          - data/corpus/scifact.jsonl
      - split: queries
        path: data/queries/fin_sci.jsonl
      - split: qrels
        path: data/qrels/fin_sci.jsonl
  - config_name: fin_bio_sci
    data_files:
      - split: corpus
        path:
          - data/corpus/fiqa.jsonl
          - data/corpus/bioasq.jsonl
          - data/corpus/scidocs.jsonl
          - data/corpus/scifact.jsonl
      - split: queries
        path: data/queries/fin_bio_sci.jsonl
      - split: qrels
        path: data/qrels/fin_bio_sci.jsonl

HVMap Dataset

HVMap Dataset is a BEIR/MTEB-style text-retrieval benchmark package for federated vector retrieval across heterogeneous vector databases. It contains the retrieval corpus, mixed-domain queries, and qrels used by the HVMap cross-domain benchmark described in Federated Vector Retrieval across Heterogeneous VectorDBs.

Repository Layout

Path Description
data/corpus/*.jsonl Search corpus shards derived from FIQA, BioASQ, SciDocs, and SciFact.
data/queries/*.jsonl Mixed-domain benchmark queries. queries.jsonl is the combined 2,000-query file.
data/qrels/*.jsonl Qrels in JSONL form. qrels.jsonl is the combined qrels file.
data/qrels/*.tsv BEIR/TREC-style qrels with header query-id corpus-id score.
metadata/dataset_manifest.json Counts, source-corpus metadata, and validation summary.

Corpus

The corpus uses retrieval-standard fields:

Field Description
_id Corpus document id. IDs are prefixed by domain, for example fin-, bio-, or sci-.
title Source title, empty when not provided by the upstream corpus.
text Searchable document text.
source_dataset Local source dataset name: fiqa, bioasq, scidocs, or scifact.
domain finance, biomedical, or science.

Corpus size:

Source dataset Domain Documents
fiqa finance 57,638
bioasq biomedical 40,221
scidocs science 25,657
scifact science 5,183
Total 128,699

Upstream metadata fields that are not needed for retrieval are not included in the corpus JSONL; the corpus is normalized to id/title/text plus source/domain fields.

Queries and Qrels

The benchmark contains 2,000 mixed-domain queries:

Dataset alias Qrels source Queries Qrels rows
fin_bio fin_bio_qrels.pickle 500 2,960
bio_sci bio_sci_qrels.pickle 500 2,786
fin_sci fin_sci_qrels.pickle 500 2,706
fin_bio_sci fin_bio_sci_qrels.pickle 500 4,414
Total 2,000 12,866

The qrels files use binary score values. Each query-document link from the source qrels is represented with score = 1.

Qrels JSONL fields:

Field Description
qrel_id Stable row id in the form {query_id}:{document_index}.
query_id Query id used in this package, such as fin_bio:0000.
corpus_id Corpus document id matching the _id field in data/corpus/*.jsonl.
score Binary qrels score.
dataset_alias Mixed-domain dataset alias.
qrels_name Source qrels pickle filename.
query_index Zero-based query index within the source qrels file.
document_index Zero-based linked-document position within that query.

Loading

Load the full benchmark:

from datasets import load_dataset

dataset = load_dataset("snu-aidas/HVMap-dataset", "all")
corpus = dataset["corpus"]
queries = dataset["queries"]
qrels = dataset["qrels"]

Load only one mixed-domain benchmark subset:

fin_bio = load_dataset("snu-aidas/HVMap-dataset", "fin_bio")
fin_bio_corpus = fin_bio["corpus"]
fin_bio_queries = fin_bio["queries"]
fin_bio_qrels = fin_bio["qrels"]

Available mixed-domain configs:

  • fin_bio: finance + biomedical corpus, fin_bio queries/qrels
  • bio_sci: biomedical + science corpus, bio_sci queries/qrels
  • fin_sci: finance + science corpus, fin_sci queries/qrels
  • fin_bio_sci: finance + biomedical + science corpus, fin_bio_sci queries/qrels

Load only a domain corpus:

finance = load_dataset("snu-aidas/HVMap-dataset", "finance", split="corpus")
biomedical = load_dataset("snu-aidas/HVMap-dataset", "biomedical", split="corpus")
science = load_dataset("snu-aidas/HVMap-dataset", "science", split="corpus")

The science config reads both scidocs and scifact corpus shards. The underlying files remain separate in the repository.

Load only combined queries or qrels:

queries = load_dataset("snu-aidas/HVMap-dataset", "queries", split="queries")
qrels = load_dataset("snu-aidas/HVMap-dataset", "qrels", split="qrels")

For BEIR-style evaluation, download data/corpus/*.jsonl, data/queries/queries.jsonl, and data/qrels/qrels.tsv. The TSV qrels files use the conventional query-id, corpus-id, score header.

Source Data and Licenses

This dataset contains mixed third-party source text. The repository metadata therefore uses license: other; users must comply with the applicable upstream licenses and attribution requirements.

Upstream source Domain role License metadata
BeIR/fiqa Financial cc-by-sa-4.0
rag-datasets/rag-mini-bioasq Biomedical cc-by-2.5
BeIR/scidocs Scientific cc-by-sa-4.0
BeIR/scifact Scientific cc-by-sa-4.0

The HVMap mixed-domain queries and qrels packaging are project-generated. The embedded source corpus text remains subject to upstream dataset licenses.

Validation

Cross-Domain Benchmark Validation

Validation consists of a human relevance assessment and two independent LLM-based consistency checks. For the human assessment, 100 queries were randomly sampled from each of the four cross-domain settings, yielding 400 queries and 2,564 associated query-document pairs. Three authors independently judged whether each document was relevant to its corresponding query.

Validation source # Queries # Pairs Relevant ratio
Annotator 1 400 2,564 99.0%
Annotator 2 400 2,564 98.8%
Annotator 3 400 2,564 98.3%
Mean pairwise agreement: 99.1%; Fleiss’ κ: 0.64
Claude Opus 4.8 2,000 12,866 94.5%
Codex GPT-5.5 2,000 12,866 93.3%

The human judgments yielded a mean pairwise agreement of 99.1% and a Fleiss’ κ of 0.64; the difference from the raw agreement reflects the highly imbalanced label distribution. As a secondary full-benchmark consistency check, Claude Opus 4.8 (max reasoning effort) and Codex GPT-5.5 (extra-high reasoning effort) independently judged all 12,866 query-document pairs across 2,000 queries. They judged 94.5% and 93.3% of the pairs as relevant, respectively.

Corpus and Qrels Integrity

Before packaging, every qrels-linked corpus_id was checked against the normalized corpus IDs included in this release.

  • Corpus document IDs are unique: 128,699 unique IDs.
  • All qrels-linked document IDs are present in the corpus.
  • Missing qrels document count: 0.
  • Qrels rows: 12,866.

Intended Use

This dataset is intended for research on text retrieval, text ranking, federated vector retrieval, heterogeneous vectorDB federation, cross-domain retrieval, and benchmark construction.

Limitations

  • The qrels use binary scores.
  • Some upstream corpus records have empty text fields in the local source corpus. These records are retained to preserve corpus id compatibility.
  • The corpus is normalized for retrieval and omits heavy upstream metadata that is not required for retrieval experiments.

Citation

If you use this dataset, cite the HVMap paper and the upstream corpora listed above. A formal BibTeX entry can be added once the HVMap paper metadata is finalized.