Update README.md

README.md

@@ -1,3 +1,62 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+# 🧮 Taxonomy Math w/ FM
+
+A high-quality mathematics dataset curated from web data using taxonomy-based filtering, containing **34 billion tokens** of mathematical content.
+
+## 🎯 Dataset Overview
+
+This dataset is part of the **EssentialWeb** project, which introduces a new paradigm for dataset curation using expressive metadata and simple semantic filters. Unlike traditional math datasets that require complex domain-specific pipelines, our approach leverages a 12-category taxonomy to efficiently identify and extract high-quality mathematical content.
+
+**🔬 Taxonomy Math w/ FM** (34B tokens): Documents labeled as `51 - Mathematics` in our taxonomy, with all 116M recalled documents then scored by the FineMath classifier and filtered to the top 34B tokens.
+
+## 🏆 Performance
+
+Our taxonomy-based approach achieves competitive results with significantly less curation effort:
+
+| Dataset | GSM8K | MATH | Curation Complexity |
+|---------|-------|------|-------------------|
+| FineMath 3+ | **26.4%** | **11.7%** | Complex domain pipeline |
+| OpenWebMath | 14.6% | 9.3% | Complex domain pipeline |
+| MegaMath Web | 9.8% | 7.9% | Complex domain pipeline |
+| Taxonomy Top Math | 21.3% | 11.0% | Simple semantic filter |
+| Taxonomy Math w/ FM | 22.4% | 11.5% | + FineMath classifier |
+
+*Results show our datasets perform within 15% of SOTA while requiring minimal domain-specific tuning.*
+
+## ✨ Key Features
+
+- **🎯 Direct Distribution Targeting**: Leverage existing taxonomy labels to target math content from web-scale data without training custom high-recall classifiers
+- **🚀 Rapid Curation**: Skip the expensive classifier training phase and go straight to content selection
+- **💰 Cost Effective**: Avoid the need to train high-recall domain-specific classifiers for content discovery
+- **🔍 Two-Stage Approach**: Use taxonomy for recall, then apply existing quality classifiers for selection
+- **🌐 Web-Scale**: Access to math content identified across 23.6B web documents
+
+## 🛠️ Curation Method
+
+Our approach simplifies math dataset creation:
+
+1. **Traditional Method**: Train high-recall classifiers → Run on billions of documents
+2. **Our Method**: Query taxonomy metadata for `51 - Mathematics` → Apply FineMath classifier to all recalled documents → Select top-scoring content
+
+### Taxonomy Category Used:
+- **Primary**: `51 - Mathematics` (FDC taxonomy label)
+- **Quality Filtering**: FineMath Classifier applied to all 116M recalled documents, selecting top 34B tokens
+
+## 🎓 Citation
+
+If you use this dataset, please cite our EssentialWeb paper:
+
+```bibtex
+@article{essentialweb2025,
+  title={EssentialWeb: A 24-Trillion Token Dataset with Expressive Metadata for Efficient Dataset Curation},
+  author={[Authors]},
+  year={2025}
+}
+```
+
+---
+
+*Part of the EssentialWeb ecosystem: Making dataset curation accessible, interpretable, and efficient.*