Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- regression
+- xgboost
+- embeddings
+- soulprint
+- sankofa
+datasets:
+- custom
+metrics:
+- mse
+- rmse
+- r2
+model-index:
+- name: Sankofa_xgb_model
+  results:
+    - task:
+        type: regression
+        name: Archetype Regression
+      metrics:
+        - name: MSE
+          type: mean_squared_error
+          value: 0.0143
+        - name: RMSE
+          type: root_mean_squared_error
+          value: 0.1198
+        - name: R²
+          type: r2_score
+          value: 0.824
+---
+
+# Sankofa XGBoost Regression Model
+
+## 📖 Model Overview
+The **Sankofa Regression Model** is part of the **Soulprint Archetype System**, designed to measure how strongly a given text reflects the values of the **Sankofa archetype**.  
+It uses **SentenceTransformer embeddings** (`all-mpnet-base-v2`) as input features and an **XGBoost regressor** trained on a **1,000-row curated dataset**.
+
+- **Architecture**: SentenceTransformer embeddings + XGBoost regression  
+- **Output Range**: 0.0 → 1.0 (Sankofa alignment score)  
+- **Training Size**: 1,000 rows (balanced distribution)  
+
+---
+
+## 🌍 What is Sankofa?
+The **Sankofa archetype** emphasizes **learning from the past, honoring ancestral wisdom, and applying history to guide future actions**.  
+- **High scores (0.7–1.0)**: Strong grounding in memory, reflection, and ancestral values  
+- **Mid scores (0.4–0.6)**: Some awareness of the past but shallow or inconsistent application  
+- **Low scores (0.0–0.3)**: Dismissal of history, impatience, or neglect of lessons from the past  
+
+---
+
+## 📊 Training & Evaluation
+
+**Training Methodology**:
+- Inputs: Free-text statements  
+- Labels: Float scores (0.0 → 1.0) for Sankofa alignment  
+- Embeddings: `all-mpnet-base-v2` from SentenceTransformers  
+- Model: XGBoost regressor  
+
+**Results**:
+- **MSE**: 0.0143  
+- **RMSE**: 0.1198  
+- **R²**: 0.824  
+
+This means predictions are typically within ±0.12 of the true score, explaining **82% of dataset variance**.
+
+---
+
+## 🚀 Intended Use
+- Measuring **alignment of text to Sankofa archetype values**  
+- Research in **Soulprint archetypes & culturally rooted AI models**  
+- Applications in **AI agents, storytelling systems, and reflective analysis tools**  
+
+---
+
+## ⚠️ Limitations
+- The dataset is **limited to 1,000 rows**; performance could improve with more data.  
+- The model is **specific to Sankofa** and should not be generalized to other archetypes.  
+- Interpretability is dependent on the embedding model (`all-mpnet-base-v2`).  
+
+---
+
+## 💡 Example Usage
+
+```python
+import joblib
+from sentence_transformers import SentenceTransformer
+
+# Load model and embedder
+model = joblib.load("Sankofa_xgb_model.pkl")
+embedder = SentenceTransformer("all-mpnet-base-v2")
+
+# Example text
+text = "The community documented their struggles so future generations could learn."
+embedding = embedder.encode([text])
+score = model.predict(embedding)[0]
+
+print("Predicted Sankofa Score:", round(float(score), 3))
+```