🕊️ 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))
Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Space using mjpsm/Imani-xgb-model 1

Evaluation results