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SGHOSH1999/bert-email-spam-classifier_tuned

sourceHugging Facemitupdated 1y agoView on Hugging Face

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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:

python

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