This project demonstrates the fine-tuning and deployment of the EleutherAI GPT-Neo 125M model for generating news articles. The dataset used for training is the "All the News" dataset from Kaggle, covering the period from 2015 to 2020. The project includes a Flask web application that allows users to generate fake news without needing to train the model themselves.

• Model: EleutherAI GPT-Neo 125M
• Dataset: All the News dataset from Kaggle
• Framework: Hugging Face Transformers
• Web Framework: Flask
• Purpose: Generate fake news articles from 2015 to 2020
• Computational Requirements: Not very intensive, suitable for an introduction to Hugging Face

1. Model Fine-Tuning: Detailed methodology and code for fine-tuning the GPT-Neo 125M model.
2. Web Application: A simple Flask web app for generating news articles without training.
3. Dataset: Focused on news articles from 2015 to 2020.

The methodology for fine-tuning the GPT-Neo 125M model on the "All the News" dataset is documented in detail in the included Jupyter notebooks and Python scripts. This includes:

1. Data Preprocessing: Cleaning and preparing the dataset for training.
2. Model Training: Fine-tuning the GPT-Neo 125M model using Hugging Face Transformers.
3. Evaluation: Assessing the model's performance and generating sample outputs.

• Python 3.7+
• Pip
• Virtual environment (recommended)

1. Install Requirements

   pip install -r requirements.txt