Spam messages frequently carry malicious links or phishing attempts posing significant threats to both organizations and their users. By choosing our RoBERTa-based spam message detection system, organizations can greatly enhance their security infrastructure. Our system effectively detects and filters out spam messages, adding an extra layer of security that safeguards organizations against potential financial losses, legal consequences, and reputational harm.

The dataset is composed of messages labeled by ham or spam, merged from three data sources:

The prepare script for enron is available at https://github.com/mshenoda/roberta-spam/tree/main/data/enron. The data is split 80% train 10% validation, and 10% test sets; the scripts used to split and merge of the three data sources are available at: https://github.com/mshenoda/roberta-spam/tree/main/data/utils.

The model is fine tuned RoBERTa base

roberta-base: https://huggingface.co/roberta-base

paper: https://arxiv.org/abs/1907.11692

Place all the files in same directory as the following:

├─── data/          contains csv data files
├─── plots/         contains metrics results and plots   
├─── roberta-spam   trained model weights 
├─── utils/         contains helper functions
├─── demo.ipynb     jupyter notebook run the demo 
├─── detector.py    SpamMessageDetector with methods train, evaluate, detect 
└─── dataset.py     custom dataset class for spam messages

To run the demo, please run the following Jupyter Notebook: demo.ipynb

** Recommend using VSCode https://code.visualstudio.com for running the demo notebook