Pulpie Orange Base

Pareto-optimal main-content extraction from HTML.
610M-parameter encoder · 0.863 ROUGE-5 F1 on WebMainBench · the balanced Pulpie model.

GitHub · Blog · PyPI

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Pulpie Orange Base extracts the main content from raw HTML, stripping navigation, ads, sidebars, and footers. It is an encoder that labels every HTML block as content or boilerplate in a single forward pass, so it approaches state-of-the-art extraction quality while running far faster and cheaper than autoregressive extractors.

At 610M parameters it sits between Orange Small and Orange Large, scoring 0.863 ROUGE-5 F1. For most use cases Orange Small offers a better speed/quality trade-off; choose Base when you want a little more headroom than Small without the cost of the 2.1B teacher.

Usage

The easiest way to use this model is through the pulpie package:

pip install pulpie

from pulpie import Extractor

extractor = Extractor(model="orange-base")
result = extractor.extract(html)

print(result.markdown)                # clean Markdown
print(result.html)                    # clean HTML
print(result.n_main, result.n_other)  # blocks kept vs dropped

Extractor auto-detects CUDA, Apple MPS, then CPU. See the GitHub README for batch and multi-GPU usage.

How it works

Pulpie runs a four-stage pipeline:

1. Simplify — remove scripts, styles, and formatting noise; tag each block with a unique ID.
2. Chunk — pack blocks into sequences of up to 8,192 tokens separated by <|sep|> markers (~80% of pages fit in one chunk).
3. Classify — a single encoder forward pass labels every block (at its <|sep|> position) as content or boilerplate.
4. Reconstruct — return the kept blocks as HTML, or convert to Markdown.

This model is a token-classification head over EuroBERT-610m, distilled from the 2.1B Pulpie Orange Large teacher (KL-divergence 0.7 + hard-label cross-entropy 0.3, temperature 2.0).

Benchmarks

WebMainBench, English subset (6,647 pages), ROUGE-5 F1:

Full analysis in the blog post.

Model family

Acknowledgements

Pulpie builds directly on the work of the MinerU-HTML and Dripper team (Ma et al., 2025). Their simplify_html preprocessing, block-level annotation scheme, and the WebMainBench benchmark are foundational to this work. Built on EuroBERT (Boizard et al., 2025).

Citation

@note{pulpie2026,
  title  = {Pulpie: Pareto-Optimal Models for Cleaning the Web},
  author = {Minhas, Bhavnick and Nigam, Shreyash and Feyn Research},
  year   = {2026},
  venue  = {Feyn Field Notes}
}

Built by Feyn. Model weights and the pulpie library are licensed under Apache 2.0.