qid
stringlengths 7
10
| topk
dict |
|---|---|
PLAIN-2
| {"MED-10":2.0,"MED-1000":null,"MED-1002":null,"MED-1003":null,"MED-1004":null,"MED-1005":null,"MED-1(...TRUNCATED)
|
PLAIN-12
| {"MED-10":null,"MED-1000":null,"MED-1002":null,"MED-1003":null,"MED-1004":0.545340836048126,"MED-100(...TRUNCATED)
|
PLAIN-23
| {"MED-10":null,"MED-1000":null,"MED-1002":null,"MED-1003":null,"MED-1004":0.6284942626953121,"MED-10(...TRUNCATED)
|
PLAIN-33
| {"MED-10":null,"MED-1000":null,"MED-1002":null,"MED-1003":null,"MED-1004":0.551060378551483,"MED-100(...TRUNCATED)
|
PLAIN-44
| {"MED-10":null,"MED-1000":null,"MED-1002":null,"MED-1003":null,"MED-1004":null,"MED-1005":null,"MED-(...TRUNCATED)
|
PLAIN-56
| {"MED-10":null,"MED-1000":null,"MED-1002":null,"MED-1003":null,"MED-1004":null,"MED-1005":0.52729356(...TRUNCATED)
|
PLAIN-68
| {"MED-10":null,"MED-1000":null,"MED-1002":null,"MED-1003":0.48018607497215204,"MED-1004":null,"MED-1(...TRUNCATED)
|
PLAIN-78
| {"MED-10":null,"MED-1000":null,"MED-1002":null,"MED-1003":null,"MED-1004":0.536332249641418,"MED-100(...TRUNCATED)
|
PLAIN-91
| {"MED-10":null,"MED-1000":null,"MED-1002":null,"MED-1003":null,"MED-1004":0.49345722794532704,"MED-1(...TRUNCATED)
|
PLAIN-102
| {"MED-10":0.505505919456481,"MED-1000":null,"MED-1002":0.49713689088821406,"MED-1003":0.526587128639(...TRUNCATED)
|
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Dataset Card
Dataset Details
This dataset contains a set of candidate documents for second-stage re-ranking on nfcorpus (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/nfcorpus-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}
}
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