BERT Spam Detection Model

Overview

This model is a fine-tuned BERT model for classifying messages as either "spam" or "ham" (non-spam). It was trained on the Telegram Spam dataset and can be used to filter out unwanted messages in various applications like email filtering systems, SMS spam detection, and maintaining the integrity of messaging platforms.

Dataset

The model was trained on the Telegram Spam dataset available on Hugging Face Datasets. The dataset contains labeled messages as either spam or ham.

Usage

To use this model, you need to install the transformers library and authenticate with your Hugging Face account if the model is private.

Installation

pip install transformers

from transformers import pipeline

# Load the model and tokenizer
model_id = 'kushalbajje/BERT'  # Replace with your model ID
classifier = pipeline('text-classification', model=model_id, tokenizer=model_id)

# Classify a sample message
message = "Congratulations! You've won a $1000 gift card. Click here to claim your prize."
result = classifier(message)
print(result)