Annotation Tool
Explore datasets on a Nomic Atlas map
Rename models in dataset leaderboard
Create and manage AI datasets for training models
Browse and view Hugging Face datasets
Create a domain-specific dataset seed
Label data for machine learning models
Display trending datasets from Hugging Face
Find and view synthetic data pipelines on Hugging Face
Label data efficiently with ease
Upload files to a Hugging Face repository
Display translation benchmark results from NTREX dataset
Support by Parquet, CSV, Jsonl, XLS
Math is a powerful annotation tool designed for dataset creation and management in machine learning workflows. It provides a streamlined platform to configure and manage datasets, enabling users to efficiently label, process, and prepare data for model training. Math is tailored for machine learning professionals and teams looking to optimize their data preparation pipelines.
• Support for Various Data Types: Easily annotate text, images, audio, and other formats. • Custom Labeling: Define and apply custom labels, tags, and categories to your dataset. • Collaboration Tools: Work with teams in real-time, assign tasks, and track progress. • Automated Annotation: Leverage AI-powered suggestions to speed up the labeling process. • Data Validation: Ensure consistency and quality with built-in validation rules. • Integration with ML Pipelines: Seamlessly export datasets to popular machine learning frameworks. • Version Control: Track changes and maintain different versions of your dataset.
What data formats does Math support?
Math supports a wide range of data formats, including text files, images, audio files, and structured data formats like JSON and CSV.
Can I use Math for real-time collaboration?
Yes, Math includes collaboration tools that allow teams to work together in real-time, with features like task assignment and progress tracking.
How does Math integrate with machine learning pipelines?
Math allows you to export datasets in formats compatible with popular machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn, making it easy to integrate with your existing workflows.