@inproceedings{beck-etal-2022-adapterhub,
title = "{A}dapter{H}ub Playground: Simple and Flexible Few-Shot Learning with Adapters",
author = "Beck, Tilman and
Bohlender, Bela and
Viehmann, Christina and
Hane, Vincent and
Adamson, Yanik and
Khuri, Jaber and
Brossmann, Jonas and
Pfeiffer, Jonas and
Gurevych, Iryna",
editor = "Basile, Valerio and
Kozareva, Zornitsa and
Stajner, Sanja",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-demo.6",
doi = "10.18653/v1/2022.acl-demo.6",
pages = "61--75",
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 \url{https://adapter-hub.github.io/playground}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters
%A Beck, Tilman
%A Bohlender, Bela
%A Viehmann, Christina
%A Hane, Vincent
%A Adamson, Yanik
%A Khuri, Jaber
%A Brossmann, Jonas
%A Pfeiffer, Jonas
%A Gurevych, Iryna
%Y Basile, Valerio
%Y Kozareva, Zornitsa
%Y Stajner, Sanja
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F beck-etal-2022-adapterhub
%X 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.
%R 10.18653/v1/2022.acl-demo.6
%U https://aclanthology.org/2022.acl-demo.6
%U https://doi.org/10.18653/v1/2022.acl-demo.6
%P 61-75
Markdown (Informal)
[AdapterHub Playground: Simple and Flexible Few-Shot Learning with Adapters](https://aclanthology.org/2022.acl-demo.6) (Beck et al., ACL 2022)
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.