Pulpie: Pareto-Optimal Models for Cleaning the Web

Pulpie is a family of Pareto-optimal models designed for extracting main content from HTML pages with high efficiency and low cost, achieving state-of-the-art (SOTA) extraction quality at a fraction of the expense. The smallest model, pulpie-orange-small, rivals the leading extractor, Dripper, with a ROUGE-5 F1 score of 0.862 while being much smaller in size and faster in processing, handling 13.7 pages per second compared to Dripper's 0.68 pages per second. This performance is attributed to Pulpie's encoder architecture, which labels HTML blocks in a single forward pass, enhancing speed and reducing costs significantly. The models, available on Hugging Face, outperform traditional extraction methods by effectively distinguishing content from boilerplate, which is crucial for both pre-training and inference processes in language models. Pulpie's development involved a novel dataset creation and a distillation process from a larger teacher model, maintaining quality while optimizing for production use. This advancement is expected to benefit large-scale data extraction tasks by providing cleaner data for training and inference, thereby improving model performance across various benchmarks.

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