didulantha/sms-spam-detector

SMS Spam Detector

Model Description

Fine-tuned DistilBERT model for detecting spam SMS messages.

Performance:

• Accuracy: 0.9916 (99.16%)
• Precision: 0.9730
• Recall: 0.9643
• F1-Score: 0.9686
• ROC-AUC: 0.9990

Quick Start

from transformers import pipeline

classifier = pipeline("text-classification", model="didulantha/sms-spam-detector")
result = classifier("Win a free prize now!")
print(result)

Detailed Usage

from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch

tokenizer = DistilBertTokenizer.from_pretrained("didulantha/sms-spam-detector")
model = DistilBertForSequenceClassification.from_pretrained("didulantha/sms-spam-detector")

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)
    return "SPAM" if probs[0][1] > 0.5 else "HAM"

print(predict("Free prize!"))

Training Data

• Dataset: SMS Spam Collection (5,574 messages)
• Train/Val/Test split: 70/15/15
• Ham: 86.6%, Spam: 13.4%

Training Details

• Base model: distilbert-base-uncased
• Epochs: 3
• Batch size: 16
• Learning rate: 2e-05
• Optimizer: AdamW

Limitations

• Trained on English SMS messages only
• May not generalize well to other languages
• Performance may degrade on heavily obfuscated spam

Citation

@misc{sms-spam-detector,
  author = {Isuru Didulantha},
  title = {SMS Spam Detector},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/didulantha/sms-spam-detector}
}