Prodigy is a downloadable annotation tool designed for creating training and evaluation data for machine learning models. Built for data scientists and developers, it supports tasks in natural language processing (NLP), computer vision, and audio processing. Prodigy offers a Python-based interface with a web front-end optimized for speed and efficiency. It integrates seamlessly with spaCy and supports custom workflows, making it suitable for rapid iteration and development.

What are the main features of Prodigy?

Flexible Annotation Workflows

Prodigy provides a variety of built-in "recipes" for different annotation tasks:

• Named Entity Recognition (NER): Manually or semi-automatically annotate entities in text.

• Text Classification: Label text data for binary or multi-class classification tasks.

• Image Annotation: Annotate images for classification, object detection, and segmentation.

• Audio and Video Annotation: Label and transcribe audio and video data.

• Dependency Parsing and Relations: Annotate syntactic dependencies and relationships between entities.

These workflows can be customized or extended using Python scripts to fit specific project needs.

Customizable Interfaces

Prodigy allows users to create custom annotation interfaces by combining built-in components or adding custom HTML, CSS, and JavaScript. This flexibility enables the development of tailored annotation experiences for various data types and tasks.

Integration with Machine Learning Models

Prodigy integrates with machine learning models to facilitate active learning:

• Model-in-the-loop Annotation: Use existing models to suggest annotations, which can then be corrected by annotators.

• Training and Evaluation: Train new models or fine-tune existing ones directly within Prodigy using annotated data.

• Exporting Data: Export annotated datasets in formats compatible with spaCy and other machine learning frameworks.

This integration streamlines the process of creating and refining models based on annotated data.

Local Deployment and Data Privacy

Prodigy is designed to run entirely on local machines, ensuring that all data remains private and secure. It does not require internet access, making it suitable for environments with strict data privacy requirements.

Why choose Prodigy?

• Efficiency: Optimized for rapid annotation, enabling quick iteration and model development.

• Flexibility: Highly customizable workflows and interfaces to suit diverse annotation tasks.

• Integration: Seamless compatibility with spaCy and support for integrating other machine learning models.

• Privacy: Local deployment ensures data remains secure and under user control.

• Extensibility: Python-based scripting allows for easy extension and customization of annotation workflows.