README.md

metadata

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:

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))