  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.
