Show HN: Pulpie – Models for Cleaning the Web

3 hours ago

Pulpie is a family of Pareto-optimal models for extracting main content from HTML pages, achieving near-state-of-the-art quality at significantly lower cost.
The smallest model, Pulpie Orange Small (210M parameters), matches Dripper's extraction quality (0.862 vs 0.864 ROUGE-5 F1) but is three times smaller and much faster (13.7 vs 0.68 pages/sec on L4 GPU).
Cost savings are substantial: cleaning 1 billion pages costs $7,900 with Pulpie vs $159,000 with Dripper on an L4 GPU.
Pulpie uses an encoder architecture that labels all HTML blocks in one forward pass, making it compute-bound and faster than decoder-based extractors like Dripper, which are memory-bandwidth-bound.
Training involved creating a dataset from Common Crawl, labeling blocks with DeepSeek V3.2, and distilling a 2.1B teacher model into smaller models (210M and 610M) via knowledge distillation.
Pulpie performs well across difficulty levels, with heuristics declining more on hard pages, and handles long pages better by chunking blocks into 8,192-token segments.
The models are open-source on Hugging Face, with easy installation and usage for both single-page and batch processing, recommending Pulpie Orange Small for production.
Clean extraction improves both pre-training (e.g., +1.08% accuracy in AICC study) and inference by reducing noise, highlighting the importance of high-quality web data.

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