extract-hyperparameters

What problem does it solve?

Manually locating and documenting all hyperparameters from research papers for model training is a meticulous and time-consuming task, prone to errors.

Core Features & Use Cases

• Parameter Extraction: Locates and documents all hyperparameters mentioned in research papers, including learning rates, batch sizes, and model configurations.
• Configuration File Generation: Helps translate extracted parameters into an implementation-ready configuration file format (e.g., YAML, JSON).
• Use Case: When reproducing results from a machine learning research paper, use this skill to quickly extract all relevant hyperparameters, such as learning rate, batch size, and optimizer type, to set up your training configuration.

Quick Start

Use the extract-hyperparameters skill to extract common ML hyperparameters like learning rate and batch size from 'paper.pdf'.