Paper to Code bridges the gap between research and implementation, enabling you to easily integrate cutting-edge techniques from academic papers into your code. Powered by OpenAI's GPT models, it automatically extracts core concepts and applies them to your codebase.
Here's a concise overview of the project's workflow:
Extract Relevant Text: The code directly extracts key sections (e.g., Introduction, Methodology) from the paper's URL, eliminating downloads and streamlining the process.
Refine Content: Unnecessary elements like reference marks and URLs are removed, ensuring focus on core concepts.
Summarize with GPT: OpenAI's GPT model summarizes the refined text, condensing key concepts for seamless integration.
Integrate into Code: The GPT model then merges the summarized concepts into your existing Python code, resulting in a new version that incorporates the paper's approach.
Save for Future Use: The integrated code is saved as a separate file, preserving the paper's methodology for future projects.
To integrate the approach into your python project, use the main.py file as a base. As an example, this repository has two folders that show different applications from this project:
cyclical-learning-rates: Within this folder, the "Cyclical Learning Rates" approach is applied to a TensorFlow-based model trained on the MNIST dataset.
layer normalization: Within this folder, the "Layer Normalization" approach is applied to a model that is slightly different. This difference was strategically created to facilitate the application of the paper.
Note: AI understanding is reinforced by well-documented code, facilitating effective decision-making during onboarding. Not only that, it is important to note that this project uses articles that propose simple concepts, as complex mathematical content or computer vision-oriented content can be difficult for AI to understand.
Both GPT-3.5 and GPT-4 produce similar results, but GPT-3.5 is the more cost-effective choice. Each code generated costs less than an eighth of a dollar and is produced in less than two minutes. To save money, a free alternative would be to use the prompts from the paper_to_code.py file in ChatGPT. However, this method requires manual intervention and is not automatic.
Although the final code might occasionally contain errors, these are usually confined to a single line. Most IDEs will readily highlight these errors, making them simple to fix.