NeuralPGRank/trec-covid-hard-negatives

Dataset Card

Dataset Details

This dataset contains a set of candidate documents for second-stage re-ranking on trec-covid (test split in BEIR). Those candidate documents are composed of hard negatives mined from gtr-t5-xl as Stage 1 ranker and ground-truth documents that are known to be relevant to the query. This is a release from our paper Policy-Gradient Training of Language Models for Ranking, so please cite it if using this dataset.

Direct Use

You can load the dataset by:

from datasets import load_dataset
dataset = load_dataset("NeuralPGRank/trec-covid-hard-negatives")

Each example is an dictionary:

>>> python dataset['test'][0]
{
    "qid" : ...,  # query ID
    "topk" : {
        doc ID: ..., # document ID as the key; None or a score as the value
        doc ID: ...,
        ...
    },
}

Citation

@inproceedings{Gao2023PolicyGradientTO,
  title={Policy-Gradient Training of Language Models for Ranking},
  author={Ge Gao and Jonathan D. Chang and Claire Cardie and Kiant{\'e} Brantley and Thorsten Joachims},
  booktitle={Conference on Neural Information Processing Systems (Foundation Models for Decising Making Workshop)},
  year={2023},
  url={https://arxiv.org/pdf/2310.04407}
}

Dataset Card Author and Contact

Ge Gao