Sift is an AI-assisted SMS scam screening project designed to help international students review unfamiliar English messages. This repository contains its primary classification model, a trained scikit-learn pipeline that predicts Normal (0) or Scam (1) and outputs a score for the Scam class. The original labeled dataset has 500 examples: 250 Scam and 250 Normal. It includes SMS-style messages, adapted messages, and some source material from other communication channels; not every example was an independently observed real-world SMS. Normal: 238 synthetic or adapted examples based on official information from organizations, including ANZ, ITV, U.S. Bank, O2, and Unity Bank, plus 12…
Independent publisher
Gwen Guan
Gwen1220
Models in Library1
Datasets in Library0
Models on Hugging Face4
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Published tasks: Text classification.
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