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--- |
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extra_gated_prompt: > |
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By accessing this dataset, you agree to comply with the original BEIR/MSMARCO |
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license, which permits usage for academic purposes only. We disclaim any |
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responsibility for copyright issues. |
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license: bigscience-openrail-m |
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language: |
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- en |
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--- |
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# ListT5-train-data |
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The dataset I used when I trained ListT5 models. |
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## License |
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This dataset adheres to the original BEIR/MSMARCO license, allowing usage solely for academic purposes. We hold no responsibility for any copyright issues. |
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## Terms of Use |
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By accessing this dataset, you agree to the following terms: |
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- The dataset is to be used exclusively for academic purposes. |
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- We are not liable for any copyright issues arising from the use of this dataset. |
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## Dataset Structure |
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## Tips for training |
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I have trained the ListT5 model for only 20k steps (20000 step) and then did early exit. |
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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 ..." |
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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. |
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## References |
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If you find this paper & source code useful, please consider citing our paper: |
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``` |
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@misc{yoon2024listt5listwisererankingfusionindecoder, |
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title={ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval}, |
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author={Soyoung Yoon and Eunbi Choi and Jiyeon Kim and Hyeongu Yun and Yireun Kim and Seung-won Hwang}, |
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year={2024}, |
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eprint={2402.15838}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IR}, |
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url={https://arxiv.org/abs/2402.15838}, |
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} |
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``` |
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## Contact |
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For further inquiries, please contact: |
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- Email: soyoung.yoon@snu.ac.kr (Soyoung Yoon) |
