extra_gated_prompt: >
By accessing this dataset, you agree to comply with the original BEIR/MSMARCO
license, which permits usage for academic purposes only. We disclaim any
responsibility for copyright issues.
license: bigscience-openrail-m
language:
- en
ListT5-train-data
The dataset I used when I trained ListT5 models.
License
This dataset adheres to the original BEIR/MSMARCO license, allowing usage solely for academic purposes. We hold no responsibility for any copyright issues.
Terms of Use
By accessing this dataset, you agree to the following terms:
- The dataset is to be used exclusively for academic purposes.
- We are not liable for any copyright issues arising from the use of this dataset.
Dataset Structure
Tips for training
I have trained the ListT5 model for only 20k steps (20000 step) and then did early exit. Referencing from the paper: "...As a result, we report the T5-base model trained for 20k steps with a learning rate of 1×10−4 and T5-3B for 3k steps with a learning rate of 1 × 10−5 ..." As a result, this result in the model running approximately 0~1 epochs of full data. The model may not need to see the whole data for training.
References
If you find this paper & source code useful, please consider citing our paper:
@misc{yoon2024listt5listwisererankingfusionindecoder,
title={ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval},
author={Soyoung Yoon and Eunbi Choi and Jiyeon Kim and Hyeongu Yun and Yireun Kim and Seung-won Hwang},
year={2024},
eprint={2402.15838},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2402.15838},
}
Contact
For further inquiries, please contact:
- Email: [email protected] (Soyoung Yoon)