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README.md ADDED
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+ monot5-3b-inpars-v2-webis-touche2020-promptagator is a monoT5-3B model finetuned on Touche-2020 synthetic data generated by [InPars](https://github.com/zetaalphavector/inPars).
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+
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+ Currently, if you use this tool you can cite the original [InPars paper published at SIGIR](https://dl.acm.org/doi/10.1145/3477495.3531863) or [InPars-v2](https://arxiv.org/abs/2301.01820).
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+
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+ ```
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+ @inproceedings{inpars,
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+ author = {Bonifacio, Luiz and Abonizio, Hugo and Fadaee, Marzieh and Nogueira, Rodrigo},
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+ title = {{InPars}: Unsupervised Dataset Generation for Information Retrieval},
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+ year = {2022},
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+ isbn = {9781450387323},
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+ publisher = {Association for Computing Machinery},
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+ address = {New York, NY, USA},
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+ url = {https://doi.org/10.1145/3477495.3531863},
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+ doi = {10.1145/3477495.3531863},
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+ booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
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+ pages = {2387–2392},
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+ numpages = {6},
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+ keywords = {generative models, large language models, question generation, synthetic datasets, few-shot models, multi-stage ranking},
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+ location = {Madrid, Spain},
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+ series = {SIGIR '22}
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+ }
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+ ```
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+
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+ ```
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+ @misc{inparsv2,
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+ doi = {10.48550/ARXIV.2301.01820},
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+ url = {https://arxiv.org/abs/2301.01820},
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+ author = {Jeronymo, Vitor and Bonifacio, Luiz and Abonizio, Hugo and Fadaee, Marzieh and Lotufo, Roberto and Zavrel, Jakub and Nogueira, Rodrigo},
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+ title = {{InPars-v2}: Large Language Models as Efficient Dataset Generators for Information Retrieval},
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+ publisher = {arXiv},
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+ year = {2023},
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+ copyright = {Creative Commons Attribution 4.0 International}
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+ }
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+ ```
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