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Register a new userFine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets
محولات ضبط الجميلة لتحديد القضايا المحتملة للإبلاغ عنها وأعراض Covid-19 في تغريدات
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نحن تصف نهجنا مباشرة إلى الأمام للمهام 5 و 6 من 2021 وسائل التواصل الاجتماعي Min - المهام المشتركة (SMM4H) المهام المشتركة.يعتمد نظامنا على DILLBERT الدقيقة على كل مهمة، وكذلك أولا ضبط النموذج على المهمة الأخرى.في هذه الورقة، نتكشف بالإضافة إلى ذلك مقدار التوصيل الدقيق ضروري لتصنيف التغريدات بدقة على النحو الذي يحتوي على أعراض Covid-19 المبلغ عنها ذاتيا (المهمة 5) أو ما إذا كانت سقسقة تتعلق Covid-19 هي التقارير الذاتية وغير الشخصية،أو أدب / أخبار ذكر الفيروس (المهمة 6).
We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Min- ing for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Dis- tillBERT on each task, as well as first fine- tuning the model on the other task. In this paper, we additionally 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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This study describes our proposed model design for SMM4H 2021 shared tasks. We fine-tune the language model of RoBERTa transformers and their connecting classifier to complete the classification tasks of tweets for adverse pregnancy outcomes (Task 4)
and potential COVID-19 cases (Task 5). The evaluation metric is F1-score of the positive class for both tasks. For Task 4, our best score of 0.93 exceeded the mean score of 0.925. For Task 5, our best of 0.75 exceeded the mean score of 0.745.
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