Label Training Data

See Extended Examples for complete configuration files and templates.

Systematically label data for supervised ML with quality controls and efficient workflows.

When to Use

• Starting supervised ML project that requires labeled training data
• Current model performance limited by insufficient labeled examples
• Need to label text, images, audio, or video data
• Want to measure and improve annotation quality
• Managing team of annotators with different expertise levels
• Implementing active learning to prioritize valuable examples
• Need to track labeling progress and costs
• Ensuring consistent labels across multiple annotators

Inputs