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
Task
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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