1024m at SMM4H 2024: Tasks 3, 5 & 6 -- Ensembles of Transformers and Large Language Models for Medical Text Classification
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Abstract (arXiv)
Social media is a great source of data for users reporting information and regarding their health and how various things have had an effect on them. This paper presents various approaches using Transformers and Large Language Models and their ensembles, their performance along with advantages and drawbacks for various tasks of SMM4H'24 - Classifying texts on impact of nature and outdoor spaces on the author's mental health (Task 3), Binary classification of tweets reporting their children's health disorders like Asthma, Autism, ADHD and Speech disorder (task 5), Binary classification of users self-reporting their age (task 6).
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References · 4
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2020Longformer: The Long-Document Transformercited by 3,413
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
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