Spamhunter

🚀 SpamHunter Model

A fine - tuned BERT model designed for efficient spam detection.

🚀 Quick Start

The SpamHunter Model is a fine - tuned BERT model crafted for spam detection. It offers high accuracy in distinguishing spam from non - spam content.

✨ Features

• High Accuracy: Achieves approximately 99% validation accuracy.
• Fine - Tuned BERT: Based on the bert - base - uncased model, fine - tuned on a custom spam emails dataset.

📦 Installation

No specific installation steps are provided in the original README. However, to use the model, you need to have the transformers library installed. You can install it using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

from transformers import BertTokenizer, BertForSequenceClassification

# Load model and tokenizer
tokenizer = BertTokenizer.from_pretrained("ar4min/SpamHunter")
model = BertForSequenceClassification.from_pretrained("ar4min/SpamHunter")

# Example
text = "Congratulations! You've won a $1000 gift card. Click here to claim now."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
prediction = outputs.logits.argmax(-1).item()

print("Spam" if prediction == 1 else "Not Spam")

📚 Documentation

Model Details