Hugging Face What is opendatalab/MinerU-HTML?
opendatalab/MinerU-HTML, also known as Dripper, is an advanced tool designed for extracting the main content from HTML web pages. Developed by OpenDatalab, this model utilizes Large Language Models (LLMs) to intelligently identify and isolate primary content, distinguishing it from boilerplate or ancillary elements. It integrates a state machine-guided generation process that uses logits processing to ensure structured JSON output, making the extracted data highly usable for downstream applications.
Key Capabilities & Features
- LLM-Powered Extraction: Employs state-of-the-art LLMs for intelligent main content identification.
- Structured Output: Uses state machine guidance with logits processing to generate structured JSON.
- Robustness: Includes an automatic fallback mechanism to alternative extraction methods in case of errors.
- Comprehensive Evaluation: Features a built-in evaluation framework supporting ROUGE and item-level metrics for performance assessment.
- Flexible Deployment: Offers a FastAPI-based REST API server for easy integration and supports distributed processing via Ray for large-scale evaluations.
- Comparison Tools: Supports multiple baseline extractors (e.g., Trafilatura, Readability, BoilerPy3) for comparative analysis.
When to Use This Model
opendatalab/MinerU-HTML is ideal for use cases requiring precise and structured extraction of main content from diverse HTML pages. This includes applications such as:
- Data Scraping: Accurately extracting article bodies, product descriptions, or news content.
- Content Summarization: Preparing clean text for summarization models by removing irrelevant HTML elements.
- Information Retrieval: Improving search relevance by focusing on core content.
- Web Archiving: Storing only the essential information from web pages.
It is particularly beneficial when high accuracy and structured output are critical, and when traditional rule-based or heuristic extractors fall short.