  This paper presents our approaches for the SMM4H24 Shared Task 5 on the
binary classification of English tweets reporting children's medical disorders.
Our first approach involves fine-tuning a single RoBERTa-large model, while the
second approach entails ensembling the results of three fine-tuned
BERTweet-large models. We demonstrate that although both approaches exhibit
identical performance on validation data, the BERTweet-large ensemble excels on
test data. Our best-performing system achieves an F1-score of 0.938 on test
data, outperforming the benchmark classifier by 1.18%.
