metadata
license: cc-by-sa-4.0
language:
- en
Dataset Card
Dataset Details
This dataset contains a set of candidate documents for second-stage re-ranking on msmarco (dev, 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/msmarco-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}
}