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