mjpsm / Bisa-xgb-model

Bisa Soulprint Regression Model

Model Details

• Model Name: Bisa Soulprint Regression (XGBoost)
• Author: Mazamesso “Mazo” Meba / Coding in Color (MyVillage Project)
• Framework: XGBoost Regressor + SentenceTransformer embeddings
• Embedding Model: all-mpnet-base-v2
• Output: Continuous score between 0.01–0.99

Intended Use

This model is part of the Soulprint Archetype system , which generates continuous scores (0.01–0.99) for the Bisa archetype . Bisa relates to endurance, resilience, and the capacity to persist within community and cultural contexts.

The model is trained on 1,308 balanced examples , evenly distributed across low (0.01–0.39), mid (0.40–0.69), and high (0.70–0.99) ratings. Inputs are short text statements (1–5 sentences), and outputs are continuous values representing Bisa intensity.

Example Applications

• Cultural storytelling: Scoring responses by levels of resilience and persistence.
• Community dialogue analysis: Identifying strength of endurance themes.
• Educational tools: Teaching Afrocentric archetype values in AI-powered environments.

Training Data

• Dataset Size: 1,308 rows
• Balance:
  • Low: 436
  • Mid: 436
  • High: 436
• Source: Synthetic, culturally-informed dataset generated using prompt engineering and controlled vocabulary/persona variation.

Labels

• Low (0.01–0.39): silence, avoidance, withholding
• Mid (0.40–0.69): mild questioning, observations, suggestions
• High (0.70–0.99): confrontation, criticism, persistence, challenge

Training Procedure

• Embeddings: all-mpnet-base-v2 SentenceTransformer
• Regressor: XGBoost
  • n_estimators=1200
  • learning_rate=0.03
  • max_depth=10
  • subsample=0.9
  • colsample_bytree=0.9
  • reg_alpha=0.5
  • reg_lambda=5

Evaluation

• Test Size: 20% split (stratified by label bins)
• Metrics:
  • MSE: 0.0202
  • R²: 0.733

This means the model explains ~73% of the variance in Bisa scores.

Comparative Context

• Other Soulprint archetype models (Nzinga, Kinara, Ubuntu) achieved R² in the 0.80–0.82 range .
• Bisa currently performs at R² = 0.73 , making it solid but with room for improvement via hyperparameter tuning or additional training data.

Limitations

• Synthetic data bias: While diverse, the dataset is synthetic and may not cover the full spectrum of cultural narratives.
• Moderate performance: R² = 0.73 means ~27% of variance is unexplained. Model works, but may misclassify edge cases.
• Context dependency: Model is tuned for Soulprint archetype research and may not generalize well to unrelated domains.

Future Work

• Hyperparameter tuning (Optuna/GridSearch for deeper optimization).
• Expanded dataset (adding 500+ new examples, including edge anchors at 0.05 and 0.95).
• Ensemble testing (combining Bisa with other archetype regressors).

Example 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/Bisa-xgb-model"
FILENAME = "Bisa_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 = "Despite noticing errors, I stayed quiet and accepted the outcome."
embedding = embedder.encode([text])
score = model.predict(embedding)[0]

print("Predicted Bisa Score:", round(float(score), 3))