AdapterHub.ml
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About
AdapterHub.ml is a powerful framework designed to streamline the adaptation of large pre-trained transformer models for various Natural Language Processing (NLP) tasks and languages. It's built on the popular HuggingFace Transformers library, allowing for the dynamic 'stitching-in' of pre-trained adapter modules. These adapters are small, efficient bottleneck layers inserted within each layer of a pre-trained model, avoiding the need for full fine-tuning of the entire model. This approach offers significant benefits by reducing storage costs and accelerating the sharing of large models. AdapterHub.ml enables parameter-efficient transfer learning, making it especially valuable for low-resource scenarios. The framework supports a wide range of tasks and languages and incorporates all recent adapter architectures, fostering scalable and accessible model sharing for the NLP community.
Platform
Task
Features
• built on huggingface transformers library
• avoids full fine-tuning of entire models
• includes recent adapter architectures
• beneficial in low-resource scenarios
• facilitates scalable and easy model sharing
• enables parameter-efficient transfer
• supports various nlp tasks and languages
• dynamic 'stitching-in' of pre-trained adapters
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