AdapterHub
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About
AdapterHub is a comprehensive framework built on HuggingFace Transformers, designed to simplify and democratize the adaptation of pre-trained transformer-based language models. It addresses the challenges of storing and sharing large models by enabling dynamic "stitching-in" of small, task-specific adapter layers. These bottleneck layers, inserted within each model layer, avoid the need for full fine-tuning, making it efficient for sharing and integrating task and language-specific knowledge. AdapterHub supports various adapter architectures and facilitates scalable access to task-specific models, particularly beneficial in low-resource NLP scenarios. It enables parameter-efficient transfer to arbitrary tasks and languages by learning modular representations, mitigating issues like the curse of multilinguality and catastrophic forgetting.
Platform
Features
• parameter-efficient cross-lingual transfer
• beneficial for low-resource nlp scenarios
• enables scalable access to task-specific models
• built on huggingface transformers library
• facilitates sharing and integrating adapter layers
• supports various adapter architectures
• avoids full fine-tuning of entire models
• dynamic "stitching-in" of pre-trained adapters
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