This paper describes our system for addressing SMM4H 2023 Shared Task 2 on
"Classification of sentiment associated with therapies (aspect-oriented)". In
our work, we adopt an approach based on Natural language inference (NLI) to
formulate this task as a sentence pair classification problem, and train
transformer models to predict sentiment associated with a therapy on a given
text. Our best model achieved 75.22\% F1-score which was 11\% (4\%) more than
the mean (median) score of all teams' submissions.

通过自然语言推理（NLI）方法，采用基于句对分类的方式预测给定文本中与治疗相关的情感，并使用 Transformer 模型进行训练，最佳模型取得 75.22% 的 F1 分数，相较于其他团队的提交，平均分数提高了 11%（中位数提高了 4%）。

一种用于疗法情感分类的通用自然语言推理方法

A Generic NLI approach for Classification of Sentiment Associated with  Therapies

The paper describes a system developed for Task 1 at SMM4H 2023. The goal of
the task is to automatically distinguish tweets that self-report a COVID-19
diagnosis (for example, a positive test, clinical diagnosis, or
hospitalization) from those that do not. We investigate the use of different
techniques for preprocessing tweets using four transformer-based models. The
ensemble of fine-tuned language models obtained an F1-score of 84.5%, which is
4.1% higher than the average value.

使用基于 Transformer 的多个模型对推文进行预处理，实现自动识别 COVID-19 诊断的推文，并获得高于平均值 4.1% 的 84.5% 的 F1 分数。