Challenges

app.py

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+<h3>Conclusion and Future Work</h3>
+
+<li>Question Answering is a complex task to understand, as it requires not only pre-processing the inputs, but also post-processing the outputs. Moreover, the metrics used are quite specific.
+<li>There is not as much documentation and tutorials available for QA as for other more popular NLP tasks. In particular, the examples provided are often focused on the SQUAD v1 format and not on SQUAD v2, the format selected for this project.
+<li>Before the Hackathon, there was no Biomedical QA dataset in Spanish publicly available (particularly with the SQUAD V2 format). It was necessary to create a validation Biomedical Dataset using the SQUAD_ES Dataset.
+</ul> 
+
 <h3>Conclusion and Future Work</h3>
 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. 
 However, the F1 Scores reported for the Biomedical roberta-based models are not far below from those of the general roberta-based model.