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arXiv · 2104.05501

Fine-Tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets

Max Fleming·Priyanka Dondeti·Caitlin N. Dreisbach·Adam Poliak
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Abstract

We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Mining for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Distill- BERT on each task, as well as first fine-tuning the model on the other task. We explore how much fine-tuning is necessary for accurately classifying tweets as containing self-reported COVID-19 symptoms (Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal reporting, or a literature/news mention of the virus (Task 6).

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Max Fleming, Priyanka Dondeti, Caitlin N. Dreisbach, Adam Poliak. 2021-04-12. Fine-Tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets. https://arxiv.org/abs/2104.05501

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