Parsing Revolution: How MinerU Quenches LLMs’ Data Thirst
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In today’s era of rapid artificial intelligence development, high-quality data has become the critical fuel driving breakthroughs in model performance, and an open-source tool called MinerU is transforming how we acquire this data.
As the demand for high-quality training data for large models continues to grow, extracting precise, structured information from massive document collections has become a key challenge.
Against this backdrop, MinerU, an intelligent data extraction tool launched by the OpenDataLab team at Shanghai AI Laboratory, is becoming a favorite among developers worldwide with its outstanding performance and innovative technical architecture.
In the field of artificial intelligence, the importance of high-quality data is self-evident. According to public information, from 2020 to 2025, the pre-training data volume for large models surged from 500 billion tokens to 36 trillion tokens, a 72-fold increase over five years.
However, public domain data on the internet is nearing exhaustion, and data convergence has led to homogenization in models.
Converting private domain data into AI-Ready data faces three major challenges: complex and diverse formats, which place extremely high demands on document parsing capabilities; high precision requirements, where minor errors can lead to decision-making biases; and high speed requirements, as industrial development demands faster processing capabilities.