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.
• modular representation learning
• support for multi-task learning
• transfer learning across languages and modalities
• lightweight adaptation through adapters
• efficient for low-resource languages
• Access to pre-trained adapters
• Dynamic adaptation of models
• Integration with HuggingFace Transformers
• Support for low-resource scenarios
Average Rating: 0.0
5 Stars:
0 Ratings
4 Stars:
0 Ratings
3 Stars:
0 Ratings
2 Stars:
0 Ratings
1 Star:
0 Ratings
No ratings available.
A federated AI framework that integrates decentralized data sources for AI development.
View Details