Build and manage datasets for machine learning
Speech Corpus Creation Tool
Evaluate evaluators in Grounded Question Answering
Upload files to a Hugging Face repository
Create a domain-specific dataset project
Organize and process datasets using AI
Transfer datasets from HuggingFace to ModelScope
Validate JSONL format for fine-tuning
Convert and PR models to Safetensors
Perform OSINT analysis, fetch URL titles, fine-tune models
Browse and view Hugging Face datasets
Generate dataset for machine learning
Support by Parquet, CSV, Jsonl, XLS
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.