metadata

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

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")