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
- Model Fine-Tuning: Detailed methodology and code for fine-tuning the GPT-Neo 125M model.
- Web Application: A simple Flask web app for generating news articles without training.
- 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:
- Data Preprocessing: Cleaning and preparing the dataset for training.
- Model Training: Fine-tuning the GPT-Neo 125M model using Hugging Face Transformers.
- Evaluation: Assessing the model's performance and generating sample outputs.
- Python 3.7+
- Pip
- Virtual environment (recommended)
-
Install Requirements
pip install -r requirements.txt