AdapterHub Playground：简单灵活的少样本学习（带适配器）
AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters
摘要：
通过在线知识库开放获取预训练语言模型的传播，导致了最先进的自然语言处理（NLP）研究的民主化。这也允许NLP之外的人使用这些模型，并使其适应特定的用例。然而，仍然需要一定的技术熟练程度，这对于想要将这些模型应用于特定任务但缺乏必要知识或资源的用户来说是一个入门障碍。在这项工作中，我们的目标是通过提供一种工具来克服这一差距，该工具允许研究人员在不编写一行代码的情况下利用预训练的模型。基于用于迁移学习的参数高效适配器模块，我们的AdapterHub Playground提供了一个直观的界面，允许使用适配器来预测，训练和分析各种NLP任务的文本数据。我们提出了该工具的架构，并展示了它的优势与原型用例，在那里我们表明，预测性能可以很容易地增加在一个少样本学习的情况下。最后，我们在用户研究中评估其可用性。我们在https://adapter-hub.github.io/playground上提供代码和实时界面。
英文摘要：
The open-access dissemination of pretrained language models through online repositories has led to a democratization of state-of-the-art natural language processing (NLP) research. This also allows people outside of NLP to use such models and adapt them to specific use-cases. However, a certain amount of technical proficiency is still required which is an entry barrier for users who want to apply these models to a certain task but lack the necessary knowledge or resources. In this work, we aim to overcome this gap by providing a tool which allows researchers to leverage pretrained models without writing a single line of code. Built upon the parameter-efficient adapter modules for transfer learning, our AdapterHub Playground provides an intuitive interface, allowing the usage of adapters for prediction, training and analysis of textual data for a variety of NLP tasks. We present the tool's architecture and demonstrate its advantages with prototypical use-cases, where we show that predictive performance can easily be increased in a few-shot learning scenario. Finally, we evaluate its usability in a user study. We provide the code and a live interface at https://adapter-hub.github.io/playground.
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。
