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%.

通过对英文推文中儿童医学疾病进行二分类，本文介绍了我们在 SMM4H24 共享任务 5 中的方法。第一种方法是对一个 RoBERTa-large 模型进行微调，而第二种方法则是对三个微调的 BERTweet-large 模型结果进行集成。我们证明尽管两种方法在验证数据上表现一致，但 BERTweet-large 集成模型在测试数据上表现出色。我们最佳的系统在测试数据上的 F1 分数达到了 0.938，超过了基准分类器 1.18%。