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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!")) |
