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Sms Spam
S
Sms Spam
Developed by akingunduz
A DistilBERT-based SMS spam classification model, fine-tuned on the sms_spam dataset, used to identify spam messages.
Downloads 25
Release Time : 4/8/2024
Model Overview
This model is a text classification model based on the DistilBERT architecture, specifically designed to distinguish between normal SMS and spam messages.
Model Features
Efficient and Lightweight
Based on the DistilBERT architecture, it is more lightweight than the full BERT model while maintaining high accuracy.
High Accuracy
Achieves a very low loss value (0.0579) on the validation set, demonstrating excellent performance.
Fast Training
Only requires 5 training epochs to achieve good results.
Model Capabilities
Text Classification
Spam Detection
English Text Processing
Use Cases
Communication Security
SMS Filtering
Automatically identifies and filters spam messages.
Effectively reduces the number of spam messages received by users.
Communication Monitoring
Monitors suspicious SMS content in communication systems.
Helps identify potential fraudulent or malicious messages.
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