🌀 Maji Archetype Regressor
This model is part of the Soulprint Archetype Framework, which measures cultural and emotional dimensions of communication.
The Maji archetype represents healing, restoration, and emotional grounding.
Model type: XGBoost regressor
Input: English text (1–5 sentences)
Output: A continuous score from 0.0–1.0
0.0–0.2 → Disconnected, dismissive, tense
0.2–0.4 → Mild support, low grounding
0.4–0.6 → Neutral, steadying presence
0.6–0.8 → Warm, supportive, moderately healing
0.8–1.0 → Deeply restorative, profound comfort
📊 Training
Dataset size: ~1,070 rows
Balanced bins: Each 0.2 range has ~210 rows
Embedding model: sentence-transformers/all-mpnet-base-v2
Regressor: XGBoost
Best config:
n_estimators=500
learning_rate=0.04
max_depth=4
subsample=0.8
colsample_bytree=0.8
🧪 Results
MSE: 0.0144
R²: 0.803
🚀 Usage
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/Maji-xgb-model" # replace if you use a different repo name
FILENAME = "Maji_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 held my sister’s hand until she calmed down."
embedding = embedder.encode([text])
score = model.predict(embedding)[0]
print("Predicted Maji Score:", round(float(score), 3))
🌍 Applications
Measuring healing energy in dialogue
Analyzing cultural communication through Soulprint
Integrating into chatbots, dashboards, or community storytelling tools