3 research outputs found
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning
We introduce Adapters, an open-source library that unifies
parameter-efficient and modular transfer learning in large language models. By
integrating 10 diverse adapter methods into a unified interface, Adapters
offers ease of use and flexible configuration. Our library allows researchers
and practitioners to leverage adapter modularity through composition blocks,
enabling the design of complex adapter setups. We demonstrate the library's
efficacy by evaluating its performance against full fine-tuning on various NLP
tasks. Adapters provides a powerful tool for addressing the challenges of
conventional fine-tuning paradigms and promoting more efficient and modular
transfer learning. The library is available via https://adapterhub.ml/adapters.Comment: EMNLP 2023: Systems Demonstration
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning
We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models. By integrating 10 diverse adapter methods into a unified interface, Adapters offers ease of use and flexible configuration. Our library allows researchers and practitioners to leverage adapter modularity through composition blocks, enabling the design of complex adapter setups. We demonstrate the library’s efficacy by evaluating its performance against full fine-tuning on various NLP tasks. Adapters provides a powerful tool for addressing the challenges of conventional fine-tuning paradigms and promoting more efficient and modular transfer learning. The library is available via https://adapterhub.ml/adapters
UKP-SQUARE: An Online Platform for Question Answering Research
Recent advances in NLP and information retrieval have given rise to a diverse
set of question answering tasks that are of different formats (e.g.,
extractive, abstractive), require different model architectures (e.g.,
generative, discriminative), and setups (e.g., with or without retrieval).
Despite having a large number of powerful, specialized QA pipelines (which we
refer to as Skills) that consider a single domain, model or setup, there exists
no framework where users can easily explore and compare such pipelines and can
extend them according to their needs. To address this issue, we present
UKP-SQUARE, an extensible online QA platform for researchers which allows users
to query and analyze a large collection of modern Skills via a user-friendly
web interface and integrated behavioural tests. In addition, QA researchers can
develop, manage, and share their custom Skills using our microservices that
support a wide range of models (Transformers, Adapters, ONNX), datastores and
retrieval techniques (e.g., sparse and dense). UKP-SQUARE is available on
https://square.ukp-lab.de.Comment: Accepted at ACL 2022 Demo Trac