Antidote Project
Collection
Data and models generated within the Antidote Project (https://univ-cotedazur.eu/antidote)
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20 items
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Updated
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5
This model is a fine-tuned version of mdeberta-v3-base for a novel extractive task which consists of identifying the explanation of the correct answer written by medical doctors. The model has been fine-tuned using the multilingual https://huggingface.co/datasets/HiTZ/casimedicos-squad dataset, which includes English, French, Italian and Spanish.
The model scores 74.64 F1 partial match (as defined in SQuAD extractive QA task) averaged across the 4 languages.
The following hyperparameters were used during training:
If you use this model please cite the following paper:
@misc{goenaga2023explanatory,
title={Explanatory Argument Extraction of Correct Answers in Resident Medical Exams},
author={Iakes Goenaga and Aitziber Atutxa and Koldo Gojenola and Maite Oronoz and Rodrigo Agerri},
year={2023},
eprint={2312.00567},
archivePrefix={arXiv}
}
Contact: Iakes Goenaga and Rodrigo Agerri HiTZ Center - Ixa, University of the Basque Country UPV/EHU