Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- regression
+- soulprint
+- tamu
+- xgboost
+- embeddings
+datasets:
+- custom
+metrics:
+- mse
+- r2
+model-index:
+- name: Tamu-xgb-model
+  results:
+  - task:
+      type: regression
+      name: Predicting Tamu Scores
+    dataset:
+      name: Soulprint Tamu Dataset
+      type: custom
+      size: 912
+    metrics:
+    - name: MSE
+      type: mse
+      value: 0.0167
+    - name: R²
+      type: r2
+      value: 0.803
+---
+
+
+
+
+# Tamu XGBoost Regression Model
+
+## Overview
+The **Tamu Regression Model** is part of the Soulprint archetype system, designed to measure expressions of *lightness, uplift, and shared resonance* in text.  
+It was trained on a **balanced dataset of 912 rows**, evenly distributed across three continuous output bins:
+
+- **Low (0.00–0.33)**: minimal energy, muted or subdued responses  
+- **Mid (0.34–0.66)**: moderate energy, rhythmic or collective responses  
+- **High (0.67–1.00)**: elevated energy, loud or vibrant expressions  
+
+The model outputs a **continuous score between 0.00 and 1.00**, where higher values correspond to stronger expressions of Tamu energy.
+
+---
+
+## Training Details
+- **Dataset size:** 912 rows (balanced: 304 per bin)  
+- **Embedding model:** `sentence-transformers/all-mpnet-base-v2`  
+- **Regressor:** XGBoost Regressor (`reg:squarederror`)  
+- **Metrics achieved:**  
+  - **MSE:** 0.0167  
+  - **R²:** 0.803  
+
+---
+
+## Usage
+### Inference Example
+```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/Tamu-xgb-model"
+FILENAME = "Tamu_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 = "Inside the library, the pages turned slowly as students whispered."
+embedding = embedder.encode([text])
+score = model.predict(embedding)[0]
+
+print("Predicted Tamu Score:", round(float(score), 3))
+```