Create README.md

README.md

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+---
+tags:
+- text-classification
+- spam-detection
+- transformers
+- bert
+datasets:
+- codesignal/sms-spam-collection
+- url
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+base_model:
+- google-bert/bert-base-cased
+pipeline_tag: text-classification
+library_name: transformers
+---
+# 📌 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
+```python
+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....