Update README.md

README.md

@@ -11,6 +11,8 @@ tags:
 - information-theory
 - entropy-maximization
 - clean-data
+- data-curation
+- pretraining
 pretty_name: Palladium-1M
 configs:
   - config_name: default
@@ -23,10 +25,61 @@ configs:
 
 **Palladium-1M** is a curated dataset of ~1 million high-entropy, high-sophistication documents (13.5GB), mined from the open web using a novel **Physics-Based Filtration System**.
 
-Unlike standard filters that rely on heuristics or keywords, the **Palladium Refinery** uses **Information Theory (ZSTD Compression Ratios)** and **Linguistic Density** to mathematically distinguish "Signal" from "Noise." 
+Unlike standard filters that rely on heuristics or keywords, the **Palladium Refinery** uses **Information Theory (ZSTD Compression Ratios)** and **Linguistic Density** to mathematically distinguish "Signal" from "Noise."
 
 The result is a dataset that trains models **significantly faster** and achieves **lower perplexity** per compute unit compared to standard web corpora (e.g., FineWeb).
 
+---
+
+## 📋 Datasheet
+
+| Metric | Value |
+|---|---|
+| **Documents (preview)** | 10,000 |
+| **Documents (full dataset)** | ~1,000,000 |
+| **Full Dataset Size** | 13.5 GB |
+| **Total Tokens (preview)** | 23,665,387 (23.7M) |
+| **Tokens/Doc (mean)** | 2,367 |
+| **Tokens/Doc (median)** | 1,296 |
+| **Tokens/Doc (range)** | 112 – 102,832 |
+| **Compression Ratio (mean)** | 2.32x |
+| **Reading Level (mean)** | Grade 11.1 |
+| **Edu Score (mean)** | 3.76 |
+| **Edu Score (median)** | 3.72 |
+| **Tokenizer** | cl100k_base (BPE) |
+
+### Domain Distribution
+
+| Domain | Docs | % |
+|---|---|---|
+| Biology / Medicine | 3,321 | 33.2% |
+| Computer Science | 1,354 | 13.5% |
+| Earth / Environmental Science | 1,245 | 12.4% |
+| General / Other | 982 | 9.8% |
+| Mathematics | 901 | 9.0% |
+| Physics | 656 | 6.6% |
+| Engineering | 588 | 5.9% |
+| Law / Policy | 379 | 3.8% |
+| Chemistry | 325 | 3.2% |
+| Economics / Finance | 181 | 1.8% |
+| Philosophy / Humanities | 68 | 0.7% |
+
+### Data Quality Visualizations
+
+![Quality Dashboard](quality_dashboard.png)
+
+![Token Distribution](token_distribution.png)
+
+![Domain Distribution](domain_distribution.png)
+
+![Edu Score Distribution](edu_score_distribution.png)
+
+![Compression Ratios](compression_distribution.png)
+
+![Grade Levels](grade_level_distribution.png)
+
+---
+
 ## 📊 The "Palladium Advantage" (Benchmark Results)
 
 To verify the quality of the data, we conducted a controlled "Battle Run" fine-tuning a **Qwen 2.5 (1.5B)** model.
@@ -35,7 +88,8 @@ To verify the quality of the data, we conducted a controlled "Battle Run" fine-t
 * **Experimental Group:** Palladium-1M (Physics-Filtered Data).
 * **Training Duration:** 1 Epoch Equivalent (30 Steps).
 
-### **Key Result: 12.5% Smarter**
+### Key Result: 12.5% Lower Loss
+
 The model trained on Palladium-1M achieved a **12.5% lower final loss** than the control group, with significantly higher training stability (lower gradient norm variance).
 
 <p align="center">
@@ -45,43 +99,52 @@ The model trained on Palladium-1M achieved a **12.5% lower final loss** than the
 | Metric | Dirty Web (FineWeb) | Palladium-1M (Clean) | Improvement |
 | :--- | :--- | :--- | :--- |
 | **Final Loss** | 2.58 | **2.26** | **-12.5%** |
-| **Gradient Stability** | High Variance | Smooth Convergence | **High** |
-| **Compute Efficiency** | Baseline | **1.2x - 1.5x** | **High** |
+| **Gradient Stability** | High Variance | Smooth Convergence | **Significant** |
 
-> **"The model wasn't fighting the data; it was absorbing it."** > — Internal Benchmarking Report, Feb 2026
+---
 
 ## 🔬 Methodology: The Physics of Information
 
 Most datasets are filtered by "Quality Classifiers" (LLMs trained to spot bad text). This is circular and expensive.
 
 **Project Palladium** takes a first-principles approach:
-1.  **Entropy Analysis:** We measure the compressibility of every document. Low entropy (highly compressible) indicates repetition, boilerplate, or SEO spam.
-2.  **Sophistication Scoring:** We map the linguistic complexity using grade-level heuristics.
-3.  **The "Goldilocks" Zone:** We discard the bottom 90% of the web that falls below a proprietary **Signal-to-Noise Threshold**.
 
-The remaining 10% is **Palladium**: Pure, dense information.
+1. **Entropy Analysis:** We measure the compressibility of every document using ZSTD compression ratios. Low entropy (highly compressible) text indicates repetition, boilerplate, or SEO spam.
+2. **Sophistication Scoring:** We map the linguistic complexity using grade-level heuristics and vocabulary density.
+3. **The "Goldilocks" Zone:** We discard the bottom ~90% of the web that falls below our Signal-to-Noise Threshold.
 
-## 🛠️ Usage
-
-This dataset is compatible with the Hugging Face `datasets` library.
-
-## 🔐 Access & Licensing
+The remaining ~10% is **Palladium**: Pure, dense information.
 
-This repository contains a **10k Document Preview**. 
+---
 
-The full **13.5GB Industrial Dataset (1M+ Docs)** is available for enterprise licensing. It is designed for:
-* **Pre-training:** Small Language Models (SLMs) that need to be data-efficient.
-* **Fine-tuning:** Specialized models for finance, law, or science.
-* **RAG Systems:** High-quality knowledge bases without the fluff.
+## 🛠️ Usage
 
-**For full access, commercial licensing, or to request custom "Refinery" services for your internal data:**
-* **Contact:** scott@palladiumtrain.com
-* **Organization:** Palladium Data
+This dataset is compatible with the Hugging Face `datasets` library.
 
 ```python
 from datasets import load_dataset
 
-# Load the Preview (Top 10k Samples)
+# Load the Preview (10K Samples)
 dataset = load_dataset("PalladiumData/Palladium-1M-Preview", split="train")
 
+print(f"Documents: {len(dataset)}")
 print(dataset[0])
+```
+
+---
+
+## 🔐 Access & Licensing
+
+This repository contains a **10,000-document preview** of the full dataset.
+
+The full **13.5GB Industrial Dataset (1M+ Docs)** is available for commercial licensing. It is designed for:
+
+* **Pre-training** small language models (1B–7B) that need to be data-efficient.
+* **Fine-tuning** specialized models for finance, law, science, or engineering.
+* **RAG systems** that need high-quality knowledge bases without boilerplate.
+
+**For full access, commercial licensing, or custom Refinery curation services:**
+
+* **Email:** [scott@palladiumtrain.com](mailto:scott@palladiumtrain.com)
+* **Web:** [palladiumtrain.com](https://www.palladiumtrain.com)
+* **Organization:** Palladium Data