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.
@article{arxiv.2108.08103,
title = {AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters},
author = {Tilman Beck and Bela Bohlender and Christina Viehmann and Vincent Hane and Yanik Adamson and Jaber Khuri and Jonas Brossmann and Jonas Pfeiffer and Iryna Gurevych},
journal= {arXiv preprint arXiv:2108.08103},
year = {2022}
}