sssdddwd/gpt2-Transfer-euact

GPT-2 Fine-Tuning on Custom Dataset

📌 Overview

This project fine-tunes a GPT-2 model on a custom dataset extracted from EU_ACT.pdf. The model is trained using transfer learning and uploaded to Hugging Face Hub for further deployment.

🚀 Features

• Uses a pre-trained GPT-2 model (Transfer Learning)
• Processes text data from a PDF
• Tokenizes and fine-tunes the model
• Uploads the trained model to Hugging Face
• Automatically disables Weights & Biases logging

📂 Project Structure

|-- fine_tune_gpt2.py   # Main script for training
|-- EU_ACT.pdf          # Custom dataset (input PDF)
|-- README.md           # Documentation
|-- image.png           # Hugging Face Metadata UI (optional)

🔧 Installation

Run the following command to install required libraries:

pip install transformers datasets torch tokenizers accelerate huggingface_hub

🔑 Hugging Face Authentication

Replace 'your-api-key-here' with your actual Hugging Face token:

from huggingface_hub import login
login(token='your-api-key-here')

🏋️‍♂️ Training the Model

Run the script to start training:

python fine_tune_gpt2.py

📤 Uploading the Model

After training, the model is automatically uploaded to Hugging Face Hub. Ensure you have set:

hf_username = "your_hf_username"  # Replace with your HF username
repo_name = "gpt2-Transfer-euact"

Pipeline

from transformers import pipeline

# Load fine-tuned model from Hugging Face Hub
repo_name = "sssdddwd/gpt2-Transfer-euact"  # Replace with your actual repo
model_pipeline = pipeline("text-generation", model=repo_name)

# Generate text
prompt = "The European AI Act aims to"
output = model_pipeline(prompt, max_length=100, num_return_sequences=1)

# Print output
print(output[0]["generated_text"])

📊 Metadata UI Reference

📌 License

You can add a license by modifying the Hugging Face license field.

📬 Contact

For issues or improvements, reach out via GitHub or Hugging Face discussions.