This library enables efficient text dataset labeling, cleaning, and enrichment using Large Language Models (LLMs). It targets ML engineers and researchers seeking to reduce manual labeling costs and time, offering high-accuracy automated labeling with customizable LLM integration.

How It Works

Autolabel streamlines data labeling through a configuration-driven, three-step process: defining labeling guidelines and LLM parameters in JSON, dry-running to validate prompts, and executing the labeling job. It supports various LLM providers (OpenAI, Anthropic, HuggingFace, Google) and advanced techniques like few-shot learning and chain-of-thought prompting to enhance label quality.

Quick Start & Requirements

• Install: pip install refuel-autolabel
• Requirements: Access to LLM APIs (e.g., OpenAI API key) or locally hosted models (vLLM compatible).
• Documentation: https://docs.refuel.ai/
• Demo: https://colab.research.google.com/drive/1t-9vNLkyoyySAG_0w3eR98biBOXlMO-E?usp=sharing

Highlighted Details

• Supports classification, question-answering, and named entity recognition tasks.
• Integrates commercial and open-source LLMs.
• Offers confidence estimation and explanations for labels.
• Includes caching and state management for cost and time efficiency.
• Provides access to Refuel-hosted open-source LLMs for calibration.

Maintenance & Community

• Active development with a public roadmap.
• Community engagement via Discord and GitHub issues.
• Links: Discord , Twitter, Website.

Licensing & Compatibility

• License: Not explicitly stated in the README.

Limitations & Caveats

The README does not specify the project's license, which may impact commercial use or integration with closed-source projects. Benchmarking details are provided, but specific performance metrics against manual labeling or other tools are not directly summarized.