自动制药新闻分类
Automatic Pharma News Categorization
摘要：
我们使用的文本数据集包括23个新闻类别相关的医药信息科学，为了比较多个Transformer模型在分类任务中的微调性能。使用具有多个自回归和自编码转换模型的平衡数据集，我们比较了它们的微调性能。为了验证获胜的方法，我们对错误预测的实例进行模型行为诊断，包括检查类别度量，评估预测确定性和评估潜在空间表示。最后，我们提出了一个集成模型，由表现最好的个人预测，并证明这种方法提供了一个适度的改进F1度量。
英文摘要：
We use a text dataset consisting of 23 news categories relevant to pharma information science, in order to compare the fine-tuning performance of multiple transformer models in a classification task. Using a well-balanced dataset with multiple autoregressive and autocoding transformation models, we compare their fine-tuning performance. To validate the winning approach, we perform diagnostics of model behavior on mispredicted instances, including inspection of category-wise metrics, evaluation of prediction certainty and assessment of latent space representations. Lastly, we propose an ensemble model consisting of the top performing individual predictors and demonstrate that this approach offers a modest improvement in the F1 metric.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
