README.md

---
language:
- en
license: apache-2.0
tags:
- text-classification
- distilbert
- clickbait
- moderation
datasets:
- marksverdhei/clickbait_title_classification
metrics:
- accuracy
- f1
- precision
- recall
---

# 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:
```json
[{'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.