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AdapterHub

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About

AdapterHub is a framework designed to simplify the adaptation of pre-trained transformer-based models for different natural language processing (NLP) tasks. It allows users to dynamically 'stitch-in' pre-trained adapters, which are small layers inserted within the transformer models, to facilitate quick sharing and integration across tasks and languages. This framework enables users to avoid performing full fine-tuning on large models, improving efficiency particularly in low-resource scenarios. It includes recent adapter architectures and is hosted at AdapterHub.ml.

Platform
Web
Keywords
machine learningnlppre-trained modelstransformer modelsadapter framework
Task
model adaptation
Features

• modular representation learning

• support for multi-task learning

• transfer learning across languages and modalities

• lightweight adaptation through adapters

• efficient for low-resource languages

Pricing Plans
Free
Free Plan

• Access to pre-trained adapters

• Dynamic adaptation of models

• Integration with HuggingFace Transformers

• Support for low-resource scenarios

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