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@@ -42,7 +42,7 @@ for the Medical Domain</h2>
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  Medical MT5-xl-multitask is a version of Medical MT5 finetuned for sequence labelling. It can correctly label a wide range of Medical labels in unstructured text, such as `Disease`, `Disability`, `ClinicalEntity`, `Chemical`... Medical MT5-xl-multitask has been finetuned for English, Spanish, French and Italian, although it may work with a wide range of languages.
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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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@@ -134,13 +134,13 @@ Our research in developing Medical mT5, a multilingual text-to-text model for th
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  # Citation
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- We will soon release a paper, but, for now, you can use:
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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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  Medical MT5-xl-multitask is a version of Medical MT5 finetuned for sequence labelling. It can correctly label a wide range of Medical labels in unstructured text, such as `Disease`, `Disability`, `ClinicalEntity`, `Chemical`... Medical MT5-xl-multitask has been finetuned for English, Spanish, French and Italian, although it may work with a wide range of languages.
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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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  # 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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  ```