AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters

Tilman Beck, Bela Bohlender, Christina Viehmann, Vincent Hane, Yanik Adamson, Jaber Khuri, Jonas Brossmann, Jonas Pfeiffer, Iryna Gurevych


Abstract
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.
Anthology ID:
2022.acl-demo.6
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
61–75
Language:
URL:
https://aclanthology.org/2022.acl-demo.6
DOI:
10.18653/v1/2022.acl-demo.6
Bibkey:
Cite (ACL):
Tilman Beck, Bela Bohlender, Christina Viehmann, Vincent Hane, Yanik Adamson, Jaber Khuri, Jonas Brossmann, Jonas Pfeiffer, and Iryna Gurevych. 2022. AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 61–75, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters (Beck et al., ACL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.acl-demo.6.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2022.acl-demo.6.mp4
Code
 adapter-hub/playground
Data
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