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ENTUM-AI/
distilbert-clickbait-classifier

Name: distilbert-clickbait-classifier
Availability: InStock

marksverdhei/clickbait_title_classification

apache-2.0

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Clickbait Classifier 🎣

This model is a fine-tuned version of distilbert-base-uncased trained to classify text (news headlines, article titles, video names) into two categories: Clickbait and Non-Clickbait.

It is optimized for filtering out sensationalist headlines and improving content recommendation algorithms.

Intended Use

The primary goal of this model is to automatically detect clickbait titles to help users and platforms prioritize high-quality informative content over misleading or exaggerated headlines.

Input: Raw English text (headlines, titles, tweets).
Return: A binary classification label (Clickbait or Non-Clickbait) with a confidence score.

Training Data

The model was fine-tuned using the bhargavasthet/clickbait_dataset, which contains a balanced collection of headlines explicitly labeled as clickbait (e.g., from Buzzfeed, Upworthy) and non-clickbait (e.g., from Reuters, The New York Times).

Performance Metrics

The model achieved excellent performance on the marksverdhei/clickbait_title_classification validation set:

Accuracy: 0.9864 (98.6%)
F1 Score: 0.9862 (98.6%)
Precision: 0.9867 (98.6%)
Recall: 0.9857 (98.5%)
Evaluation Loss: 0.0488

Training Constraints & Hyperparameters

The model was trained under the following conditions:

Base Architecture: distilbert-base-uncased (chosen for speed and efficiency)
Maximum Sequence Length: 128
Learning Rate: 2e-05
Batch Size: 64
Precision: Mixed Precision (fp16)
Optimizer Strategy: Early Stopping (patience=3)
Epochs: 3

Usage 🚀

You can easily integrate this model into your applications using the Hugging Face transformers library pipeline:

python

from transformers import pipeline

# Load the clickbait classifier
classifier = pipeline("text-classification", model="ENTUM-AI/distilbert-clickbait-classifier")

# Test with a sensational headline
text_1 = "10 Bizarre Facts About Apples That Will BLOW YOUR MIND! 🍎🤯"
result_1 = classifier(text_1)
print(f"Text: '{text_1}'\nPrediction: {result_1}\n")

# Test with a normal news headline
text_2 = "Apple releases new quarterly earnings report showing 5% growth."
result_2 = classifier(text_2)
print(f"Text: '{text_2}'\nPrediction: {result_2}")

Expected Output format:

[{'label': 'Clickbait', 'score': 0.9921}]

Potential Applications

📰 News Aggregators: Filter out low-quality clickbait articles.
📱 Social Media Feeds: Demote clickbait posts in recommendation algorithms.
✉️ Email Spam Filters: Detect clickbait-style subject lines in promotional emails.