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---
tags:
- spam
- classification
- bert
- pytorch
- comment-filter
- text-classification
- content-moderation
- social-media
license: mit
language: en
datasets: custom
widget:
- text: "Click here to win a free iPhone!"
- text: "Great video, thanks for sharing!"
- text: "Follow me for daily crypto tips 💰"
- text: "This tutorial saved my life, thank you!"
- text: "🔥 Get rich quick! Limited-time offer!"
---
# 📦 Spam Detector — `vibehq/spam-detector`
A BERT-based spam classifier fine-tuned to detect **spam and promotional content** in social media-style comments. Trained on real-world-like comment data including giveaways, scams, promotions, and genuine engagement.
Perfect for content moderation on platforms like:
- YouTube
- Instagram
- Discord
- Reddit
- Facebook
- Forums or blogs
---
## 🚀 How to Use
```python
from transformers import BertTokenizer, BertForSequenceClassification
import torch
# Load model and tokenizer
model = BertForSequenceClassification.from_pretrained("vibehq/spam-detector")
tokenizer = BertTokenizer.from_pretrained("vibehq/spam-detector")
def predict_spam(comment):
inputs = tokenizer(comment, return_tensors='pt', max_length=128, padding='max_length', truncation=True)
with torch.no_grad():
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=-1).item()
return "Spam" if prediction == 1 else "Non-Spam"
# Example
print(predict_spam("Subscribe to my channel for more giveaways!")) |