This tutorial demonstrates workflows to incorporate text data into actuarial classification and regression tasks. The main focus is on methods employing transformer-based models. A dataset of car accident descriptions with an average length of 400 words, available in English and German, and a dataset with short property insurance claims descriptions are used to demonstrate these techniques. The case studies tackle challenges related to a multi-lingual setting and long input sequences. They also show ways to interpret model output, to assess and improve model performance, by fine-tuning the models to the domain of application or to a specific prediction task. Finally, the tutorial provides practical approaches to handle classification tasks in situations with no or only few labeled data, including but not limited to ChatGPT. The results achieved by using the language-understanding skills of off-the-shelf natural language processing (NLP) models with only minimal pre-processing and fine-tuning clearly demonstrate the power of transfer learning for practical applications.

翻译：本教程展示了将文本数据融入精算分类和回归任务的工作流程。重点在于采用基于Transformer模型的方法。通过一个平均长度为400词、包含英语和德语版本的交通事故描述数据集，以及一个简短财产保险理赔描述数据集，来演示这些技术。案例研究解决了多语言环境和长输入序列带来的挑战。同时，通过微调模型以适应应用领域或特定预测任务，展示了如何解读模型输出、评估并提升模型性能。最后，本教程提供了在无标注数据或仅有少量标注数据情况下处理分类任务的实用方法，包括但不限于ChatGPT。通过仅需最少的预处理和微调，即可利用现成自然语言处理（NLP）模型的语言理解能力所取得的成果，清晰展示了迁移学习在实际应用中的强大力量。