Jaber Khuri
2022
AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters
Tilman Beck
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Bela Bohlender
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Christina Viehmann
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Vincent Hane
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Yanik Adamson
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Jaber Khuri
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Jonas Brossmann
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Jonas Pfeiffer
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Iryna Gurevych
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
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.
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Co-authors
- Tilman Beck 1
- Bela Bohlender 1
- Christina Viehmann 1
- Vincent Hane 1
- Yanik Adamson 1
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