---
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
library_name: bertopic
tags:
- code
---

# SpamHunter Model

This is a fine-tuned BERT model for spam detection.

## Model Details
- **Base Model**: bert-base-uncased
- **Dataset**: Custom spam emails dataset
- **Training Steps**: 3 epochs
- **Validation Accuracy**: ~99%

## How to Use

### Direct Integration with Transformers
```python
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")