Datasetpublic

irds/antique_train

Dataset Card for antique/train The antique/train dataset, provided by the ir-datasets package. For more information about the dataset, see the documentation. Data This dataset provides: queries (i.e., topics); count=2,426 qrels: (relevance assessments); count=27,422 For docs, use irds/antique Usage from datasets import load_dataset queries = load_dataset('irds/antique_train', 'queries') for record in queries: record # {'query_id': ...… See the full description on the dataset page: https://huggingface.co/datasets/irds/antique_train.

sourceHugging Faceupdated 4y agoView on Hugging Face

0likes23downloads

Dataset Card

Dataset Card for `antique/train`

The antique/train dataset, provided by the ir-datasets package. For more information about the dataset, see the documentation.

Data

This dataset provides:

—queries (i.e., topics); count=2,426
—qrels: (relevance assessments); count=27,422

—For docs, use `irds/antique`

Usage

python

from datasets import load_dataset

queries = load_dataset('irds/antique_train', 'queries')
for record in queries:
    record # {'query_id': ..., 'text': ...}

qrels = load_dataset('irds/antique_train', 'qrels')
for record in qrels:
    record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}

Note that calling load_dataset will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format.

Citation Information

@inproceedings{Hashemi2020Antique,
  title={ANTIQUE: A Non-Factoid Question Answering Benchmark},
  author={Helia Hashemi and Mohammad Aliannejadi and Hamed Zamani and Bruce Croft},
  booktitle={ECIR},
  year={2020}
}