Jul 15, 2020 · We propose AdapterHub, a framework that allows dynamic "stitching-in" of pre-trained adapters for different tasks and languages.
We propose AdapterHub, a framework that allows dynamic “stiching-in” of pre-trained adapters for different tasks and languages.
1) We propose an easy-to-use and extensible adapter training and sharing frame- work for transformer-based models such as BERT,. RoBERTa, and XLM(-R); 2) we ...
AdapterHub builds on the HuggingFace transformers framework, requiring as little as two additional lines of code to train adapters for a downstream task. Latest ...
AdapterHub is a framework simplifying the integration, training and usage of adapters and other efficient fine-tuning methods for Transformer-based language ...
AdaptersHub is proposed, a framework that allows dynamic “stiching-in” of pre-trained adapters for different tasks and languages that enables scalable and ...
Adapters is an add-on library to HuggingFace's Transformers, integrating 10+ adapter methods into 20+ state-of-the-art Transformer models with minimal coding ...
A technical framework of DCT-based GIS vector data compression is proposed. DCT is applied to transform vector data into combination of cosine curves, while in ...
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AdapterHub.ml makes working with adapters accessible by providing a framework for training, sharing, discovering and consuming adapter modules. Check out these ...
AdapterHub consists of two core components: 1) A library built on top of HuggingFace transformers, and 2) a website that dynamically provides analysis and ...