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deberta-v2
question-answering
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mDeBERTa-base for Multilingual Correct Explanation Extraction in the Medical Domain

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.

Performance

The model scores 74.64 F1 partial match (as defined in SQuAD extractive QA task) averaged across the 4 languages.

  • tags: to delimit explanations of the correct answers and the rest at token level.
    • 0: explanation of the correct answer
    • 1: others

Fine-tuning hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 48
  • eval_batch_size: 8
  • seed: random
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20.0

Framework versions

  • Transformers 4.30.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.2

Citation

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

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