RoBERTa based Spam Message Detection
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.
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Metrics
| Loss | Accuracy(0.9906) | Precision(0.9971) / Recall(0.9934) | Confusion Matrix |
|---|---|---|---|
 Train / Validation |
 Validation |
 Validation |
 Testing Set |
Model Output
- 0 is ham
- 1 is spam
Dataset
https://huggingface.co/datasets/mshenoda/spam-messages
The dataset is composed of messages labeled by ham or spam, merged from three data sources:
- SMS Spam Collection https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
- Telegram Spam Ham https://huggingface.co/datasets/thehamkercat/telegram-spam-ham/tree/main
- Enron Spam: https://huggingface.co/datasets/SetFit/enron_spam/tree/main (only used message column and labels)
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.
Dataset Class Distribution
| Training 80% | Validation 10% | Testing 10% |
|---|---|---|
 Class Distribution |
 Class Distribution |
 Class Distribution |
Architecture
The model is fine tuned RoBERTa
roberta-base: https://huggingface.co/roberta-base
paper: https://arxiv.org/abs/1907.11692
Code
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