Build and manage datasets for machine learning
Generate synthetic datasets for AI training
Label data for machine learning models
Upload files to a Hugging Face repository
Browse TheBloke models' history
Organize and process datasets efficiently
Display trending datasets from Hugging Face
Create and manage AI datasets for training models
Display instructional dataset
Upload files to a Hugging Face repository
Upload files to a Hugging Face repository
Curate and manage datasets for AI and machine learning
Validate JSONL format for fine-tuning
Tbilisi AI Lab Annotation is a specialized tool designed for building and managing datasets for machine learning applications. It provides a user-friendly interface to annotate, label, and organize data efficiently, enabling researchers and developers to prepare high-quality datasets for training AI models. This tool is essential for structuring raw data into a format that is ready for use in machine learning workflows.
• Dataset Creation: Build datasets from scratch or import existing data for annotation. • Data Labeling: Assign labels, tags, and descriptions to data points for supervised learning. • Support for Multiple Data Types: Handle text, images, audio, and video data seamlessly. • Collaboration Tools: Work with teams to annotate datasets in real-time. • Version Control: Track changes and maintain different versions of datasets. • Export Options: Export datasets in formats compatible with popular machine learning frameworks. • Quality Assurance: Implement data validation rules to ensure consistency and accuracy.
What types of data can I annotate with Tbilisi AI Lab Annotation?
Tbilisi AI Lab Annotation supports a wide range of data types, including text, images, audio, and video, making it versatile for various machine learning applications.
Can I collaborate with multiple users on the same dataset?
Yes, the tool offers real-time collaboration features, allowing teams to work together on annotating datasets. You can assign different roles and permissions to team members.
How do I export my dataset for machine learning?
After annotating and validating your dataset, you can export it in formats such as CSV, JSON, or directly integrate it with popular machine learning frameworks like TensorFlow or PyTorch.