Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- regression
+- xgboost
+- soulprint
+- imani
+datasets:
+- custom
+metrics:
+- mse
+- r2
+model-index:
+- name: Imani XGBoost Regression Model
+  results:
+  - task:
+      type: regression
+      name: Soulprint Archetype Scoring
+    dataset:
+      name: Imani-regression-data
+      type: custom
+    metrics:
+    - name: MSE
+      type: mse
+      value: 0.00866
+    - name: R²
+      type: r2
+      value: 0.892
+---
+
+# 🕊️ Imani XGBoost Regression Model
+
+This model is part of the **Soulprint archetype system**, designed to measure the presence of the **Imani (Faithful)** archetype in text.  
+It outputs a **score between 0.0 and 1.0** that reflects the degree of *faith, resilience, and affirmation* expressed.
+
+- **Framework:** XGBoost  
+- **Embeddings:** SentenceTransformer (`all-mpnet-base-v2`)  
+- **Training Data Size:** 819 samples  
+- **Balanced dataset:** Low, mid, and high Imani scores evenly distributed (~33% each)  
+
+---
+
+## 🧾 Model Details
+
+- **Archetype:** Imani (Faithful)  
+- **Description:** Sacred conviction and hope, even in adversity.  
+- **Traits captured:** Encouraging, spiritual, consistent, compassionate.  
+- **Perspective:** *Faith is the seed, action is the rain.*  
+- **Output range:** `0.0 – 1.0`  
+
+---
+
+## 📊 Training Results
+
+- **MSE:** `0.00866`  
+- **R²:** `0.892`  
+
+These metrics indicate that the model is highly accurate, with predictions averaging less than `0.1` away from true labels on the 0–1 scale.
+
+---
+
+## 🚀 Usage
+
+You can load the model directly from Hugging Face Hub and run predictions:
+
+```python
+import xgboost as xgb
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import hf_hub_download
+
+# -----------------------------
+# 1. Download model from Hugging Face
+# -----------------------------
+REPO_ID = "mjpsm/Imani-xgb-model"
+FILENAME = "Imani_xgb_model.json"
+
+model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+
+# -----------------------------
+# 2. Load Model + Embedder
+# -----------------------------
+model = xgb.XGBRegressor()
+model.load_model(model_path)
+
+embedder = SentenceTransformer("all-mpnet-base-v2")
+
+# -----------------------------
+# 3. Example Prediction
+# -----------------------------
+text = "I reminded my cousin that storms always pass."
+embedding = embedder.encode([text])
+score = model.predict(embedding)[0]
+
+print("Predicted Imani Score:", round(float(score), 3))
+```