ENTUM-AI/roberta-clickbait-classifier

🎯 RoBERTa Clickbait Classifier

A clickbait detection model built on RoBERTa-base (125M parameters), fine-tuned on multiple combined and deduplicated English datasets.

🚀 Quick Start

from transformers import pipeline

classifier = pipeline("text-classification", model="ENTUM-AI/roberta-clickbait-classifier")

# Clickbait
result = classifier("You Won't BELIEVE What This Celebrity Did Next!")
print(result)  # [{'label': 'Clickbait', 'score': 0.99...}]

# Non-Clickbait
result = classifier("Federal Reserve raises interest rates by 0.25 percentage points")
print(result)  # [{'label': 'Non-Clickbait', 'score': 0.99...}]

Model Details

Architecture	RoBERTa-base (125M parameters)
Task	Binary text classification
Labels	Clickbait (1), Non-Clickbait (0)
Language	English
License	Apache 2.0
Max input length	128 tokens

📊 Training Data

Three public English clickbait datasets, combined and deduplicated:

Dataset	Source
christinacdl/Clickbait_New	58.6K samples from multiple sources
marksverdhei/clickbait_title_classification	32K samples (Chakraborty et al., ASONAM 2016)
contemmcm/clickbait	26K samples

After deduplication and balancing: ~48K samples (train/val/test split 85/10/5).

⚙️ Training

Fine-tuned with HuggingFace Trainer using linear LR schedule with warmup, AdamW optimizer, and early stopping on F1 score.

💡 Use Cases

• News aggregators — filter low-quality clickbait articles
• Social media — content moderation and feed quality scoring
• Browser extensions — warn users about clickbait headlines
• Email filters — detect clickbait-style subject lines
• Content platforms — automated content quality assessment

⚠️ Limitations

• English only
• Optimized for short texts (headlines, titles, tweets); longer texts will be truncated to 128 tokens
• Reflects patterns and biases present in the training data sources