Ads G P T2

Introduction

AdsGPT2 is a fine-tuned version of the GPT-2 model, tailored for generating advertisement content. It is trained on over 10,000 programmatic ad creatives to provide inspiration and a starting point for ad design. The model generates ads based on input product or brand names, aiming to inspire creative ideas in advertising.

Architecture

AdsGPT2 builds upon the GPT-2 architecture, a transformer-based model known for its ability to generate coherent and contextually relevant text. The fine-tuning process adapts GPT-2 to specialize in ad content generation.

Training

Training Data

The model was trained on a diverse dataset of 10,000+ programmatic ad creatives sourced from various ad platforms. This dataset includes a wide range of products, brands, and ad styles.

Training Procedure

AdsGPT2 was obtained by fine-tuning the GPT-2 base model using the specified training data. This process allows the model to understand and generate relevant ad content.

Evaluation

Traditional evaluation metrics like accuracy or F1 score are not applicable due to the creative nature of the task. The model's effectiveness is assessed informally based on the relevance and creativity of its outputs.

Safety and Bias Considerations

The model inherits safety and bias concerns from GPT-2. It may produce content that is offensive or inappropriate and reflect biases present in training data. Users should review outputs for alignment with brand values.

Guide: Running Locally

1. Environment Setup: Ensure that you have Python and PyTorch installed. You can use Anaconda or pip to manage packages.
2. Install Hugging Face Transformers:

   pip install transformers
   

3. Download the Model: Access the model via Hugging Face's model hub, using the transformers library.
4. Load and Use the Model:

   from transformers import GPT2LMHeadModel, GPT2Tokenizer
   
   model = GPT2LMHeadModel.from_pretrained("PeterBrendan/AdsGPT2")
   tokenizer = GPT2Tokenizer.from_pretrained("PeterBrendan/AdsGPT2")
   
   input_text = "Nike Air Force Ones"
   inputs = tokenizer(input_text, return_tensors="pt")
   outputs = model.generate(**inputs, max_length=50)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   

Suggest Cloud GPUs

For enhanced performance, consider using cloud-based GPUs such as AWS EC2, Google Cloud Platform, or Azure.

License

AdsGPT2 is distributed under the MIT License, allowing for free use, modification, and distribution with proper attribution.