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@@ -193,21 +193,19 @@ We translate the BioASQ-6B English Question Answering dataset to generate parall
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  We translate the `body`, `snippets`, `ideal_answer` and `exact_answer` fields. We have validated the quality of the `ideal_answer` field, however, the `exact_answer` field can contain translation artifacts, as NLLB200 often produces low-quality translations of single-word sentences.
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  </p>
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- - 📖 Paper: [Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain]()
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  - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
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  - Original Dataset: [http://bioasq.org/participate/challenges_year_6](http://bioasq.org/participate/challenges_year_6)
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  - Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR
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  ## Citation
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-
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- We will soon release a paper, but, for now, you can use:
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-
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  ```bibtext
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- @inproceedings{medical-mt5,
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- title = "{{Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain}}",
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- author = "{Iker García-Ferrero and Rodrigo Agerri and Aitziber Atutxa Salazar and Elena Cabrio and Iker de la Iglesia and Alberto Lavelli and Bernardo Magnini and Benjamin Molinet and Johana Ramirez-Romero and German Rigau and Jose Maria Villa-Gonzalez and Serena Villata and Andrea Zaninello}",
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- publisher = "Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING)",
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- year = 2024 }
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-
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- ```
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-
 
 
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  We translate the `body`, `snippets`, `ideal_answer` and `exact_answer` fields. We have validated the quality of the `ideal_answer` field, however, the `exact_answer` field can contain translation artifacts, as NLLB200 often produces low-quality translations of single-word sentences.
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  </p>
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+ - 📖 Paper: [Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain](https://arxiv.org/abs/2404.07613)
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  - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
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  - Original Dataset: [http://bioasq.org/participate/challenges_year_6](http://bioasq.org/participate/challenges_year_6)
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  - Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR
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  ## Citation
 
 
 
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  ```bibtext
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+ @misc{garcíaferrero2024medical,
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+ title={Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain},
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+ author={Iker García-Ferrero and Rodrigo Agerri and Aitziber Atutxa Salazar and Elena Cabrio and Iker de la Iglesia and Alberto Lavelli and Bernardo Magnini and Benjamin Molinet and Johana Ramirez-Romero and German Rigau and Jose Maria Villa-Gonzalez and Serena Villata and Andrea Zaninello},
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+ year={2024},
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+ eprint={2404.07613},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```