Sathya77/spam-ham-classifier

📌 Spam Classifier (BERT Fine-Tuned)

Introduction

This is my first fine-tuned model on Hugging Face 🚀. It is a spam vs ham (not spam) classifier built using a BERT model fine-tuned on SMS spam data. The goal is to help detect unwanted spam messages while keeping normal communications intact. I created and uploaded this model as part of my learning journey into NLP and Transformers.
The model was trained on a spam/ham dataset with high accuracy and strong F1 performance.
It can be used for SMS filtering, email pre-screening, or any application requiring spam detection.

📖 Model Details

• Architecture: BERT base (bert-base-cased)
• Task: Binary Text Classification
• Labels: 0 = ham, 1 = spam
• Dataset: Custom spam/ham dataset (e.g., SMS Spam Collection)
• Fine-tuned epochs: 3
• Framework: Hugging Face Transformers

🧪 Evaluation Results

Metric	Score
Accuracy	99.3%
F1 Score	97.5%
Precision	100%
Recall	95.1%

🚀 How to Use

from transformers import pipeline

classifier = pipeline("text-classification", model="Sathya77/spam-ham-classifier")

classifier("Congratulations! You won a free gift card!")
# → [{'label': 'spam', 'score': 0.99}]

🚀 Limitations and Future Work

• May not generalize perfectly to domains outside SMS/email.
• Some borderline spam messages may still be misclassified.
• Future improvements: larger training data, multilingual support.

Thank You For Supporting me....