scientific-transfer-learning

What problem does it solve?

Transforms scientific data tasks into transferable learning workflows by enabling domain adaptation and knowledge distillation for small datasets.

Core Features & Use Cases

• Fine-tune pre-trained models on domain-specific science data to improve accuracy with limited labeled samples.
• Perform few-shot / zero-shot learning, domain adaptation, and multi-task learning to generalize across tasks and datasets.
• Apply knowledge distillation and model transfer to deploy compact, efficient models in science workflows.

Quick Start

Select a pre-trained model and a science dataset, then run the finetuning workflow to obtain a tuned model.