Ad Text Generation

Introduction

The AdTextGeneration model is designed to generate advertising copy, specifically fine-tuned for Amazon shopping categories like electronics and wearables. It is built on the EleutherAI/gpt-neo-125M model, utilizing the PyTorch library and Hugging Face's Transformers for text generation tasks.

Architecture

The core architecture of the AdTextGeneration model is based on GPT-Neo, a transformer-based architecture known for its text generation capabilities. The model has been fine-tuned for specific categories to enhance its performance in generating coherent and relevant ad copy.

Training

The model has been fine-tuned on datasets focused on electronics and wearable products to optimize its performance in these areas. It employs various parameters for inference, such as a temperature of 0.7, a maximum length of 200 tokens, and sampling techniques with top_k set to 5 and top_p set to 0.9.

Guide: Running Locally

To run the AdTextGeneration model locally, follow these steps:

1. Clone the Repository: Start by cloning the repository from Hugging Face to your local machine.
2. Install Dependencies: Ensure you have Python and PyTorch installed. Use pip to install the Hugging Face Transformers library.
3. Load the Model: Use the Transformers library to load the model and tokenizer.
4. Generate Text: Input prompts such as product names to generate ad text.

For better performance, especially with larger datasets or more complex tasks, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The AdTextGeneration model is released under the MIT License, allowing for flexibility in usage, modification, and distribution.