SGHOSH1999/bert-email-spam-classifier_tuned

BERT Email Spam Classifier

This repository contains a fine-tuned BERT model for SMS/email spam detection using the SMS Spam Collection Dataset.

Accuracy:

Loss: 0.012 Accuracy: 0.98

Model Details

• Architecture: BERT (Bidirectional Encoder Representations from Transformers)
• Task: Binary classification (spam vs. ham)
• Base Model: bert-base-uncased
• Framework: Hugging Face Transformers (PyTorch)
• License: MIT

Usage

Load the model and tokenizer using Hugging Face Transformers:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("SGHOSH1999/bert-email-spam-classifier_tuned")
model = AutoModelForSequenceClassification.from_pretrained("SGHOSH1999/bert-email-spam-classifier_tuned")

Training Data

• SMS Spam Collection Dataset
• The dataset contains 5,574 SMS messages labeled as 'ham' (legitimate) or 'spam'.

How to Cite

If you use this model, please cite the original dataset and this repository.

License

MIT