自然语言处理中预训练-微调范式教程
A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing
AI总结：
本教程介绍面向文本分析的预训练-微调范式，讲解其基本概念、实践流程及在分类和回归任务中的应用，并开放代码与数据集。
摘要：
鉴于自然语言是表达思想和情感的主要媒介，文本分析已成为心理学研究中的一项关键技术。它能够从自然语言中提取有价值的洞见，为人格特质评估、心理健康监测以及人际沟通中的情感分析等工作提供便利。在文本分析中，现有研究往往采用人工编码、预构建词典或从零开始训练模型的方式：人工编码耗时费力，预构建词典通常无法覆盖所有可能的场景，而从零开始训练模型则需要大量标注数据。在本教程中，我们介绍预训练-微调范式。预训练-微调范式代表了文本分析和自然语言处理中一种具有变革意义的方法。该范式通过使用大型预训练语言模型而独具特色，即使在训练数据有限的情况下，也能在微调任务中表现出显著的效率。这种效率对于社会科学研究尤其有益，因为这些研究中的标注样本数量通常十分有限。我们的教程全面介绍了预训练-微调范式。我们首先深入讲解预训练和微调的基本概念，随后结合真实应用开展实践练习。我们展示了该范式在多类分类和回归等多种任务中的应用。为强调其有效性和易用性，本教程旨在鼓励更广泛地采用这一范式。为此，我们已开放所有代码和数据集的访问权限。该教程对心理学的各个学科都大有裨益，为在多样化研究场景中运用文本分析提供了全面指南。
英文摘要：
Given that natural language serves as the primary conduit for expressing thoughts and emotions, text analysis has become a key technique in psychological research. It enables the extraction of valuable insights from natural language, facilitating endeavors like personality traits assessment, mental health monitoring, and sentiment analysis in interpersonal communications. In text analysis, existing studies often resort to either human coding, which is time-consuming, using pre-built dictionaries, which often fails to cover all possible scenarios, or training models from scratch, which requires large amounts of labeled data. In this tutorial, we introduce the pretrain-finetune paradigm. The pretrain-finetune paradigm represents a transformative approach in text analysis and natural language processing. This paradigm distinguishes itself through the use of large pretrained language models, demonstrating remarkable efficiency in finetuning tasks, even with limited training data. This efficiency is especially beneficial for research in social sciences, where the number of annotated samples is often quite limited. Our tutorial offers a comprehensive introduction to the pretrain-finetune paradigm. We first delve into the fundamental concepts of pretraining and finetuning, followed by practical exercises using real-world applications. We demonstrate the application of the paradigm across various tasks, including multi-class classification and regression. Emphasizing its efficacy and user-friendliness, the tutorial aims to encourage broader adoption of this paradigm. To this end, we have provided open access to all our code and datasets. The tutorial is highly beneficial across various psychology disciplines, providing a comprehensive guide to employing text analysis in diverse research settings.
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。
