from setfit import SetFitModel
import pickle, numpy as np

model = SetFitModel.from_pretrained("faysal725/product-review-sentiment-classifier")

# For calibrated confidence, download platt_calibrator.pkl from this repo
with open("platt_calibrator.pkl", "rb") as f:
    platt = pickle.load(f)

text = "This product is amazing, best purchase ever!"
raw_probs = np.array(model.predict_proba([text]))
calibrated = platt.predict_proba(raw_probs[:, 1].reshape(-1, 1))[0]
label = "positive" if calibrated[1] >= 0.5 else "negative"
confidence = max(calibrated)

print(f"{label} ({confidence:.1%})")

Training

Method: SetFit (few-shot contrastive learning, 256 examples)
Backbone: paraphrase-mpnet-base-v2
Calibration: Platt scaling (LogisticRegression on raw scores)
Data: Amazon Reviews Binary (Apache-2.0), 10k balanced subset

Full Package

For the complete package with FastAPI server, batch endpoint, Docker deployment, and full documentation, visit Gumroad.

License

MIT