diff --git a/BENCHMARKS.md b/BENCHMARKS.md
index dfa46d8..93ade5f 100644
--- a/BENCHMARKS.md
+++ b/BENCHMARKS.md
@@ -20,23 +20,44 @@ Ground truth: `convert_main_content` field (html2text with bodywidth=0, ignore_l
### Notes
-- **pg/s**: pages/second on a single A100 80GB GPU.
-- **Pulpie models** use a naive sequential inference loop (no batching). Throughput can be improved with batched inference.
+- **pg/s** (this table): pages/second during the full WebMainBench quality run on a
+ single A100 80GB, with Pulpie using a naive sequential loop (no batching). The
+ batched throughput numbers in the section below are the ones to use for
+ speed/cost — batching roughly 6x's Pulpie Small on A100 (4.0 → 25.7).
+- **Dripper** ([MinerU-HTML v1.1](https://huggingface.co/opendatalab/MinerU-HTML-v1.1-hunyuan0.5B-compact)) uses vLLM with guided regex decoding for batched inference.
- **Dripper** ([MinerU-HTML v1.1](https://huggingface.co/opendatalab/MinerU-HTML-v1.1-hunyuan0.5B-compact)) uses vLLM with guided regex decoding for batched inference.
- **Empty**: pages where the method produced no output (context overflow for Dripper, no blocks detected for Pulpie).
- **magic-html**: [opendatalab/magic-html](https://github.com/opendatalab/magic-html) rule-based extractor.
- **Trafilatura**: [trafilatura](https://github.com/adbar/trafilatura) with `include_tables=True`.
- **Raw html2text**: full HTML passed directly through html2text (no extraction).
+### Throughput (500 real Common Crawl pages)
+
+L4 throughput:
+
+| Method | Pages/sec (L4) | Pages/sec (A100, batched) |
+|--------|----------------|---------------------------|
+| Pulpie Small | **13.7** | 25.7 |
+| Pulpie Base | 3.9 | 7.7 |
+| Pulpie Large | 1.3 | 3.5 |
+| Dripper | 0.68 | 3.6 |
+
+Pulpie Small runs **20x faster than Dripper on L4** and **7.1x faster on A100**.
+
### Cost comparison (1B pages, projected)
-| Setup | pg/s | GPU-hours | Cost (RunPod) |
-|-------|------|-----------|---------------|
-| Pulpie Small on L4 | 15.1 | 18,400 | $6,500 |
-| Pulpie Small on A100 | 4.0 (unbatched) | 69,400 | $9,700 |
-| Dripper on A100 (vLLM) | 3.5 | 79,400 | $77,000 |
+L4 at $0.39/hr, using the throughputs above:
+
+| Setup | Pages/sec | GPU-hours / 1B | Cost / 1B pages |
+|-------|-----------|----------------|-----------------|
+| Pulpie Small on L4 | 13.7 | 20,300 | **~$7,900** |
+| Pulpie Base on L4 | 3.9 | 71,200 | ~$28,000 |
+| Pulpie Large on L4 | 1.3 | 214,000 | ~$83,000 |
+| Dripper on L4 | 0.68 | 408,000 | ~$159,000 |
+
+Pulpie Small matches Dripper quality (0.862 vs 0.864) at **~20x lower cost** on an L4.
-Pulpie Small matches Dripper quality (0.862 vs 0.864) at ~12x lower cost.
+Full analysis: [Pulpie: Pareto-Optimal Models for Cleaning the Web](https://usefeyn.com/blog/pulpie-pareto-optimal-models-for-cleaning-the-web/).
### Hardware
diff --git a/LICENSE b/LICENSE
new file mode 100644
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@@ -0,0 +1,202 @@
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diff --git a/README.md b/README.md
index 6c68b9e..67d9d80 100644
--- a/README.md
+++ b/README.md
@@ -1,76 +1,157 @@
-
-
-
-
-
-
+
-Fast content extraction from HTML using encoder models. 16x faster than autoregressive approaches at the same quality.
+
+
+
+
-## Install
+[](https://pypi.org/project/pulpie/)
+[](https://pypi.org/project/pulpie/)
+[](https://github.com/chonkie-inc/pulpie/blob/main/LICENSE)
+[](https://pepy.tech/project/pulpie)
+[](https://usefeyn.com/blog/pulpie-pareto-optimal-models-for-cleaning-the-web/)
+[](https://github.com/chonkie-inc/pulpie/stargazers)
+
+_Pareto-optimal models for cleaning the web — extract main content from HTML at one twentieth the cost._
+
+[Install](#installation) •
+[Usage](#usage) •
+[Models](#models) •
+[How it works](#how-it-works) •
+[Benchmarks](#benchmarks) •
+[Blog](https://usefeyn.com/blog/pulpie-pareto-optimal-models-for-cleaning-the-web/)
+
+
+
+Pulpie extracts the main content from raw HTML — stripping navigation, ads, sidebars, and footers — using small encoder models that label every block in a single forward pass. It approaches state-of-the-art extraction quality while running up to **20x faster** and **20x cheaper** than autoregressive extractors.
+
+**⚡ Fast** — an encoder labels every block in one forward pass (13.7 pages/sec on an L4)
+**🎯 Accurate** — matches SOTA quality: 0.862–0.873 ROUGE-5 F1 on WebMainBench
+**🪶 Small** — the recommended model is 210M params, fits on any GPU
+**💸 Cheap** — clean 1 billion pages for ~$7,900 vs ~$159,000 for the leading decoder
+**📦 Simple** — `pip install pulpie`, then `Extractor().extract(html)`
+**🔌 Batched** — overlapped CPU+GPU pipeline scales across multiple GPUs
+
+## Installation
```bash
pip install pulpie
```
-For markdown output:
+For Markdown output, install the `markdown` extra:
+
+```bash
+pip install "pulpie[markdown]"
+```
+
+Or with [uv](https://docs.astral.sh/uv/):
+
```bash
-pip install pulpie[markdown]
+uv pip install "pulpie[markdown]"
```
## Usage
+### Basic
+
```python
from pulpie import Extractor
-extractor = Extractor() # downloads pulpie-orange-small (210M) on first use
-
+extractor = Extractor() # defaults to pulpie-orange-small (210M)
result = extractor.extract(html)
-print(result.markdown) # clean markdown
-print(result.html) # clean HTML
-print(result.n_main) # number of content blocks
-print(result.n_other) # number of boilerplate blocks
-```
-## Models
+print(result.markdown) # clean Markdown
+print(result.html) # clean HTML
+print(result.n_main, result.n_other) # blocks kept vs dropped
+```
-| Model | Size | ROUGE-5 | Speed (L4) |
-|-------|------|---------|------------|
-| `orange-small` | 210M | 0.864 | 15 pps |
-| `orange-base` | 610M | 0.849 | ~6 pps |
-| `orange-large` | 2.1B | 0.862 | ~2 pps |
+The model downloads from Hugging Face on first use.
-`orange-small` is the default and recommended model — it matches the 2.1B teacher at 1/10th the size.
+### Choosing a model
```python
-# Use a specific model
-extractor = Extractor(model="orange-large")
+extractor = Extractor(model="orange-large") # "orange-small" (default), "orange-base", "orange-large"
+extractor = Extractor(model="path/to/model") # or a custom checkpoint
+extractor = Extractor(device="cpu") # force CPU
+```
+
+### Batch processing
-# Use a custom model path
-extractor = Extractor(model="path/to/your/model")
+For bulk extraction, `Pipeline` overlaps CPU preprocessing with GPU inference and self-balances across one or more GPUs:
-# Force CPU
-extractor = Extractor(device="cpu")
+```python
+from pulpie import Pipeline, PageInput
+
+pipeline = Pipeline(model="orange-small")
+results = pipeline.extract_batch(
+ [PageInput(html=h, page_id=i) for i, h in enumerate(pages)]
+)
```
+## Models
+
+All three models are built on [EuroBERT](https://arxiv.org/abs/2503.05500), share a tokenizer, and use the same `<|sep|>` block-marker architecture. Large is the teacher; Base and Small are distilled from it.
+
+| Model | Hugging Face | Params | ROUGE-5 F1 | Notes |
+|-------|--------------|--------|------------|-------|
+| **Orange Small** | [`chonkie-ai/pulpie-orange-small`](https://huggingface.co/chonkie-ai/pulpie-orange-small) | 210M | 0.862 | **Recommended** — best size-to-quality ratio |
+| Orange Base | [`chonkie-ai/pulpie-orange-base`](https://huggingface.co/chonkie-ai/pulpie-orange-base) | 610M | 0.863 | Distilled from Large |
+| Orange Large | [`chonkie-ai/pulpie-orange-large`](https://huggingface.co/chonkie-ai/pulpie-orange-large) | 2.1B | 0.873 | Teacher (highest quality) |
+
+`orange-small` is the default. Despite being a third the size of Dripper (the leading extractor), it matches its quality (0.862 vs 0.864) while running 20x faster.
+
## How it works
-Pulpie classifies each HTML block as "main content" or "boilerplate" using a bidirectional encoder. The pipeline:
+Pulpie keeps the "read the page" approach of model-based extractors but moves the bottleneck from memory bandwidth to compute by using an encoder instead of a decoder. The pipeline runs in four stages:
+
+1. **Simplify** — remove scripts, styles, and formatting noise; tag each content block with a unique ID.
+2. **Chunk** — split, tokenize, and pack blocks into chunks of up to 8,192 tokens (≈80% of pages fit in one chunk).
+3. **Classify** — a single encoder forward pass labels every block as content or boilerplate.
+4. **Reconstruct** — return the kept blocks as HTML, or convert them to Markdown.
+
+A decoder emits labels one token at a time, re-reading the full model from GPU memory each step. An encoder runs one dense forward pass over the whole input — so the gap widens on bandwidth-limited GPUs (7x faster than Dripper on A100, 20x on L4).
-1. **Simplify** — Strip scripts, styles, normalize HTML (via MinerU-HTML)
-2. **Chunk** — Pack blocks into sequences separated by `<|sep|>` tokens
-3. **Classify** — Single encoder forward pass classifies all blocks simultaneously
-4. **Reconstruct** — Extract content blocks, convert to markdown
+## Benchmarks
-## Performance
+Quality on the English subset of [WebMainBench](https://github.com/opendatalab/WebMainBench) (6,647 pages), ROUGE-5 F1:
-On 500 real Common Crawl pages (NVIDIA L4 GPU):
+| Method | Params | ROUGE-5 F1 | Empty pages |
+|--------|--------|------------|-------------|
+| **Pulpie Orange Large** | 2.1B | **0.873** | 21 |
+| Dripper | 0.6B | 0.864 | 135 |
+| **Pulpie Orange Base** | 610M | 0.863 | 36 |
+| **Pulpie Orange Small** | 210M | 0.862 | 45 |
+| magic-html | — | 0.700 | 384 |
+| Trafilatura | — | 0.619 | 16 |
-- **15.1 pages/sec** (single GPU, 210M model)
-- **$6,500** to clean 1 billion pages
-- **16.4x faster** than Dripper (autoregressive) on the same hardware
-- **433 MB** VRAM — fits on any GPU
+Speed and cost (Pulpie Orange Small vs Dripper, 1 billion pages):
+
+| | Pulpie Orange Small | Dripper |
+|--|--------------------|---------|
+| Throughput (L4) | **13.7 pages/sec** | 0.68 pages/sec |
+| Cost / 1B pages (L4) | **~$7,900** | ~$159,000 |
+
+Pulpie Orange Small matches Dripper's quality at **20x the throughput** and **20x lower cost** on an L4. See [BENCHMARKS.md](BENCHMARKS.md) for the full comparison, per-difficulty breakdown, and reproduction command.
+
+## 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. We also use their Dripper 0.6B model to cross-validate our training labels. We're grateful they released their tools and data.
+
+## Citation
+
+If you use Pulpie in your research, please cite:
+
+```bibtex
+@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}
+}
+```
-## License
+---
-Apache 2.0
+
+Built by Chonkie, the open-source work behind Feyn.
+
-