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app.py
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<h3>Conclusion and Future Work</h3>
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However, the F1 Scores reported for the Biomedical roberta-based models are not far below from those of the general roberta-based model.
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If only unanswerable questions are taken into account, the model with the best F1 Score is <a href="https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-es-squad2-es">hackathon-pln-es/roberta-base-biomedical-es-squad2-es</a>.
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The model <a href="https://huggingface.co/hackathon-pln-es/biomedtra-small-es-squad2-es">hackathon-pln-es/biomedtra-small-es-squad2-es</a>, on the contrary, shows inability to correctly identify unanswerable questions.
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As future work, the following experiments could be carried out:
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</ul>
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<h3>Conclusion and Future Work</h3>
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If F1 Score is considered, the results show that there may be no advantage in using domain-specific masked language models to generate Biomedical QA models.
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However, the F1 Scores reported for the Biomedical roberta-based models are not far below from those of the general roberta-based model.
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If only unanswerable questions are taken into account, the model with the best F1 Score is <a href="https://huggingface.co/hackathon-pln-es/roberta-base-biomedical-es-squad2-es">hackathon-pln-es/roberta-base-biomedical-es-squad2-es</a>.
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The model <a href="https://huggingface.co/hackathon-pln-es/biomedtra-small-es-squad2-es">hackathon-pln-es/biomedtra-small-es-squad2-es</a>, on the contrary, shows inability to correctly identify unanswerable questions.
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As future work, the following experiments could be carried out:
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