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