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📰 Fake News Detection using DistilBERT

🚀 Binary Classification of Fake and Real News using Transformer Models

📌 Project Overview

This project uses a DistilBERT transformer model to classify news articles as real or fake. The dataset is sourced from Kaggle and consists of labeled news articles. The model achieves high accuracy in detecting misinformation.

📂 Dataset

  • Source: Fake and Real News Dataset
  • Structure:
    • True.csv → Real news articles
    • Fake.csv → Fake news articles
  • Preprocessing:
    • Combined title and text columns for better context.
    • Added labels (1 = Real, 0 = Fake).
    • Tokenized text using DistilBERT Tokenizer.

⚙️ Installation

Clone this repository and install dependencies:

git clone https://github.com/your_username/fake-news-detection.git
Open Powershell in windows and run:
pip install -r "path/to/requirements.txt"