Build and manage datasets for machine learning
Build datasets using natural language
Create a report in BoAmps format
Upload files to a Hugging Face repository
Provide feedback on AI responses to prompts
Label data for machine learning models
Generate synthetic datasets for AI training
Evaluate evaluators in Grounded Question Answering
Upload files to a Hugging Face repository
Explore and edit JSON datasets
Manage and orchestrate AI workflows and datasets
Manage and annotate datasets
Browse and extract data from Hugging Face datasets
Tbilisi AI Lab Annotation is a cutting-edge tool designed for building and managing datasets for machine learning applications. It provides a comprehensive platform for data annotation, enabling users to create high-quality training data efficiently. This tool is particularly useful for data scientists, machine learning engineers, and researchers who need to prepare datasets for various AI models.
• Intuitive Annotation Interface: Streamline the annotation process with a user-friendly interface.
• Multi-Format Support: Handle diverse data types, including text, images, and videos.
• Collaborative Workflow: Invite team members to collaborate in real-time for faster dataset creation.
• Data Validation: Ensure consistency and accuracy with built-in validation checks.
• Integration with ML Pipelines: Seamlessly export annotated data to machine learning workflows.
What types of data can I annotate with Tbilisi AI Lab Annotation?
You can annotate text, images, and videos, making it suitable for a wide range of machine learning tasks.
Can I collaborate with others in real-time?
Yes, Tbilisi AI Lab Annotation supports real-time collaboration, allowing multiple users to work on the same dataset simultaneously.
How do I export annotated data?
Once your annotations are complete, you can export the dataset in formats such as CSV, JSON, or COCO for direct integration into machine learning pipelines.