Prodigy · An Annotation Tool for AI, Machine Learning & NLP

What's Prodigy?

Prodigy is a powerful and scriptable annotation tool that revolutionizes the way data scientists approach data labeling. Designed by the creators of spaCy, Prodigy integrates state-of-the-art machine learning techniques with user-friendly interfaces, allowing rapid iteration and model training. Its active learning capabilities ensure that you only annotate data the model needs, speeding up the annotation process and improving model accuracy.

Features

Named Entity Recognition

Efficiently identify and label names, concepts, and phrases in your text data.

Span Categorization

Handle overlapping and nested spans with ease, ensuring comprehensive text annotation.

Text Classification

Label both short and long texts, enabling detailed and accurate categorization.

Dependencies & Relations

Connect words and phrases to understand the relationships in your data.

Computer Vision

Classify and segment images, bringing visual data into your AI workflows.

Audio & Video

Annotate audio and video files to incorporate multimedia data into your projects.

Model Training

Quickly train new models, leveraging modern machine learning techniques.

A/B Evaluation

Conduct fast and rigorous experiments to optimize your models.

Task Routing

Efficiently distribute annotation tasks among your team.

Prompt Engineering

Utilize tests and tournaments to refine prompts for large language models.

Large Language Models

Integrate Prodigy with spaCy-LLM for advanced language model capabilities.

Use Case

Prodigy is designed for data scientists and AI practitioners who need a flexible and efficient tool for data annotation. Whether you're working on natural language processing, computer vision, or any other machine learning project, Prodigy simplifies the data labeling process. By enabling rapid iteration and minimizing the amount of data needed for training, Prodigy helps you bring your models to production faster.

FAQ

How does Prodigy streamline the annotation process?

Prodigy uses active learning to present only the data points that the model is uncertain about, reducing the amount of data you need to annotate and speeding up the process.

Can Prodigy be integrated into my existing workflow?

Yes, Prodigy is fully scriptable and integrates seamlessly with Python-based data science workflows, including popular frameworks like spaCy, PyTorch, and TensorFlow.

What types of data can I annotate with Prodigy?

Prodigy supports a wide range of data types, including text, images, audio, and video, making it a versatile tool for various AI and machine learning projects.

Is Prodigy suitable for large-scale projects?

Absolutely. Prodigy is designed to handle projects of any scale, from small experiments to large, complex datasets. Its task routing and model training features ensure efficient management of large-scale annotation efforts.

How to Use

To get started with Prodigy, you can try the live demo available on the Prodigy website. The demo showcases various annotation tasks, including entity recognition, text categorization, and image bounding boxes. For detailed instructions and documentation, visit the Prodigy documentation page.

Experience the efficiency and power of Prodigy by getting your copy today.