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Ayo-xgb-model
mjpsm/Ayo-xgb-model
Ayo-xgb-model is a machine learning model from mjpsm. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for xgboost. The card lists the license as mit.
- Type: XGBoost Regressor - Embeddings: all-mpnet-base-v2 (SentenceTransformer) - Trained on: Ayo Soulprint dataset (~1,100 rows, balanced bins) - Target: Continuous score from 0.00 → 1.00
Access
Public
Updated Sep 29, 2025
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At a glance
- Library
- xgboost
- License
- mit
- Access
- Public
- Created
- Sep 29, 2025
- Updated
- Sep 29, 2025
- SHA
- ab9a564d
Datasets
Model card
From the Hugging Face model README
Ayo Soulprint Regression Model
Model Details
- Type: XGBoost Regressor
- Embeddings: all-mpnet-base-v2 (SentenceTransformer)
- Trained on: Ayo Soulprint dataset (~1,100 rows, balanced bins)
- Target: Continuous score from 0.00 → 1.00
Performance
- MSE: 0.0154
- R²: 0.801
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/Ayo-xgb-model"
FILENAME = "Ayo_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 = "The crowd cheered loudly as the drums pounded."
embedding = embedder.encode([text])
score = model.predict(embedding)[0]
print("Predicted Ayo Score:", round(float(score), 3))
- Library
- xgboost
- License
- mit
- Languages
- en
- Created
- Sep 29, 2025
- Updated
- Sep 29, 2025