You can deploy this model and start earning money today!

Saman11233/
clickbait-distilbert-detector

Name: clickbait-distilbert-detector
Rating: 2.5 (26 reviews)

distilbert/distilbert-base-uncased

finetune

apache-2.0

Views

No views yet

🚀 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.

👤 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

🛠️ 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)

📊 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.

💻 How to Use in Python

Option 1: Quick Pipeline Inference

python

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)

python

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)")

📌 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.