readme.md

metadata

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

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!"))