LabelLLM is an open-source platform designed to streamline and enhance the data annotation process for Large Language Models (LLMs). It targets independent developers and small to medium-sized research teams, offering a unified solution for efficient, high-quality data preparation across multimodal datasets.

How It Works

LabelLLM employs a flexible, configurable framework with task-specific tools adaptable to diverse annotation needs. It supports multimodal data (audio, images, video) within a single platform and features a comprehensive task management system for real-time monitoring and quality control. The platform also integrates AI-assisted pre-annotation, allowing users to refine AI-generated labels for increased efficiency and accuracy.

Quick Start & Requirements

• Installation: Local deployment via Docker Compose (docker compose up).
• Prerequisites: Docker, Linux recommended.
• Access: Web UI at localhost:9001 (default credentials: user/password). Backend API at http://localhost:8086.
• Resources: Initial installation may take time; requires a good internet connection.
• Docs: Deployment Tutorial Video, Backend Configuration, Frontend Configuration.

Highlighted Details

• Supports multimodal data annotation (audio, images, video).
• AI-assisted pre-annotation for enhanced efficiency.
• Comprehensive task management with quality control.
• Flexible and customizable task-specific tools.

Maintenance & Community

The project is part of the opendatalab ecosystem, which also includes LabelU and MinerU. Citation details are provided in BibTeX format.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The platform is primarily recommended for Linux environments. Specific details regarding licensing and commercial use are not provided in the README, which may pose a barrier for some users.