Saman11233/clickbait-distilbert-detector

🚀 Clickbait Headline Detection System

An end-to-end NLP classification model built using DistilBERT to accurately identify whether a given headline or title is Clickbait or Not Clickbait.

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👤 Author & Student Metadata

• Developer / Author: RAJA SAAD ALI
• Roll Number: SU72-BSCSM-F25-011
• Program / Degree: BS Computer Science (BSCS)
• Project Title: Clickbait Headline Detection System

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🛠️ Model Architecture & Training Setup

• Base Architecture: distilbert-base-uncased
• Task Type: Binary Sequence Classification
• Frameworks: PyTorch, Hugging Face transformers, datasets
• Max Sequence Length: 128 tokens
• Optimizer: AdamW
• Output Classes:
  • LABEL_0: NOT CLICKBAIT (Genuine / Standard News)
  • LABEL_1: CLICKBAIT (Sensationalized / Engagement Bait)

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📊 Dataset & Evaluation Metrics

• Dataset: Clickbait Detection Dataset (Balanced News Titles and Social Media Headlines)
• Evaluation Focus: High Accuracy, Precision, and Recall across both real and clickbait samples.

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💻 How to Use in Python

Option 1: Quick Pipeline Inference

from transformers import pipeline

# Load classifier
classifier = pipeline("text-classification", model="Saman11233/clickbait-distilbert-detector")

# Test sample
sample_headline = "10 Secrets Doctors Don't Want You to Know!"
result = classifier(sample_headline)

print(result)

Option 2: Interactive Testing Loop (Google Colab)

from transformers import pipeline

classifier = pipeline("text-classification", model="Saman11233/clickbait-distilbert-detector")

user_input = input("Enter headline: ")
if user_input.strip():
    res = classifier(user_input)[0]
    label = "CLICKBAIT" if res["label"] == "LABEL_1" else "NOT CLICKBAIT"
    print(f"Prediction: {label} ({res['score']*100:.2f}% confidence)")

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📌 Intended Use & Limitations

• Intended Use: Automating content moderation, news feed curation, and assisting users in filtering clickbait content.
• Limitations: Performs best on English news titles and short social media posts.