Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- regression
+- xgboost
+- kinara
+- soulprint
+- coding-in-color
+- myvillage
+model-index:
+- name: Kinara Regression Model
+  results:
+  - task:
+      type: regression
+      name: Kinara Score Prediction
+    dataset:
+      name: Soulprint Synthetic Kinara Dataset
+      type: jsonl
+      size: ~1.2k rows
+    metrics:
+    - name: MSE
+      type: mean_squared_error
+      value: 0.0086
+    - name: RMSE
+      type: root_mean_squared_error
+      value: 0.0927
+    - name: R²
+      type: r2
+      value: 0.8856
+---
+
+# Kinara Regression Model
+
+The **Kinara Regression Model** is part of the *Soulprint Archetypes* framework developed under the MyVillage Project / Coding in Color initiative.  
+It predicts a **Kinara score** (0–1) for any input text, where Kinara reflects *purpose, spirit, and vision* — inspired by the Kwanzaa candleholder and symbolic elders such as Fannie Lou Hamer.
+
+## 🔑 Archetype Background
+According to the [Soulprint Archetypes Document](/), **Kinara** is:
+
+- *Adjective*: Guiding  
+- *Description*: Kinara is the keeper of purpose, spirit, and vision. They mentor, anchor, and align groups with mission and integrity.  
+- *Catchphrase*: *Hold the light high.*
+
+## 📊 Model Details
+- **Type**: XGBoost Regressor  
+- **Embeddings**: [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) sentence embeddings  
+- **Dataset**: ~1,210 rows synthetic + curated data (balanced, strict-unique)  
+- **Target**: Kinara Score (continuous between 0 and 1)
+
+### Training Metrics
+- **MSE**: `0.0086`  
+- **RMSE**: `0.0927`  
+- **R² Score**: `0.8856`
+
+These metrics indicate a strong fit with low error and high explanatory power.
+
+## 🚀 Usage
+
+First, install dependencies:
+
+```bash
+pip install xgboost sentence-transformers joblib
+Then load and use the model:
+import joblib
+from sentence_transformers import SentenceTransformer
+
+# Load model from Hugging Face
+model = joblib.load("mjpsm/Kinara_xgb_model/Kinara_xgb_model.pkl")
+embedder = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
+
+# Example input
+text = "During a heated family argument, I stayed calm and reminded everyone of our values."
+X = embedder.encode([text])
+pred = model.predict(X)
+
+print(f"Predicted Kinara Score: {pred[0]:.2f}")
+```
+
+## Example Predictions
+
+- Input: “During a heated family argument, I stayed calm and reminded everyone of our values.”
+ Output: 0.75
+
+- Input: “I hesitated when a classmate asked for advice, and they left still confused.”
+ Output: 0.24
+
+- Input: “At a neighborhood gathering, I shared a story about unity that brought people closer.”
+ Output: 0.75