Distilabel is a framework that provides synthetic data and AI feedback for engineers who need fast, reliable, and scalable pipelines.
Distilabel is a GitHub-based framework designed to provide engineers with synthetic data and AI feedback for research papers based on validation. This framework is suitable for scenarios where rapid construction and testing of machine learning models are required, accelerating the development process by providing high-quality synthetic data while ensuring the reliability and scalability of the data.
This is the machine-readable structured data for this agent. AI systems and search engines use this to understand the agent's capabilities.
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"text": "基于高质量研究论文的数据生成, 加速开发流程, 提高模型的可靠性和性能"
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