Vaccination is important to minimize the risk and spread of various diseases. In recent years, vaccination has been a key step in countering the COVID-19 pandemic. However, many people are skeptical about the use of vaccines for various reasons, including the politics involved, the potential side effects of vaccines, etc. The goal in this task is to build an effective multi-label classifier to label a social media post (particularly, a tweet) according to the specific concern(s) towards vaccines as expressed by the author of the post. We tried three different models-(a) Supervised BERT-large-uncased, (b) Supervised HateXplain model, and (c) Zero-Shot GPT-3.5 Turbo model. The Supervised BERT-large-uncased model performed best in our case. We achieved a macro-F1 score of 0.66, a Jaccard similarity score of 0.66, and received the sixth rank among other submissions. Code is available at-https://github.com/anonmous1981/AISOME

翻译：疫苗接种对于降低各种疾病的风险和传播至关重要。近年来，疫苗接种已成为应对COVID-19大流行的关键措施。然而，由于政治因素、疫苗潜在副作用等多种原因，许多人仍对疫苗的使用持怀疑态度。本任务的目标是构建一个有效的多标签分类器，根据推文作者表达的具体疫苗相关顾虑，对社交媒体帖子（特别是推文）进行标签分类。我们尝试了三种不同的模型：（a）有监督的BERT-large-uncased模型，（b）有监督的HateXplain模型，以及（c）零样本GPT-3.5 Turbo模型。其中，有监督的BERT-large-uncased模型在我们的实验中表现最佳。我们取得了0.66的宏观F1分数和0.66的Jaccard相似度分数，并在所有提交结果中排名第六。代码地址为：https://github.com/anonmous1981/AISOME