🕊️ 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))