Create README.md

README.md

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+---
+language: en
+library_name: xgboost
+tags:
+- regression
+- soulprint
+- ayo
+- xgboost
+datasets:
+- custom
+metrics:
+- mse
+- r2
+model_name: Ayo XGB Soulprint Model
+model_file: Ayo_xgb_model.json
+license: mit
+---
+
+# Ayo Soulprint Regression Model
+
+## Model Details
+- **Type:** XGBoost Regressor
+- **Embeddings:** all-mpnet-base-v2 (SentenceTransformer)
+- **Trained on:** Ayo Soulprint dataset (~1,100 rows, balanced bins)
+- **Target:** Continuous score from 0.00 → 1.00
+
+## Performance
+- **MSE:** 0.0154  
+- **R²:** 0.801  
+
+## Usage
+```python
+import xgboost as xgb
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import hf_hub_download
+
+# -----------------------------
+# 1. Download model from Hugging Face Hub
+# -----------------------------
+REPO_ID = "mjpsm/Ayo-xgb-model"
+FILENAME = "Ayo_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 = "The crowd cheered loudly as the drums pounded."
+embedding = embedder.encode([text])
+score = model.predict(embedding)[0]
+
+print("Predicted Ayo Score:", round(float(score), 3))
+```