The goal of this competition is to understand the relationship between code and comments in Python notebooks. You are challenged to reconstruct the order of markdown cells in a given notebook based on the order of the code cells, demonstrating comprehension of which natural language references which code.

### Context

Research teams across Google and Alphabet are exploring new ways that machine learning can assist software developers, and want to rally more members of the developer community to help explore this area too. Python notebooks provide a unique learning opportunity, because unlike a lot of standard source code, notebooks often follow narrative format, with comment cells implemented in markdown that explain a programmer's intentions for corresponding code cells. An understanding of the relationships between code and markdown could lend to fresh improvements across many aspects of AI-assisted development, such as the construction of better data filtering and preprocessing pipelines for model training, or automatic assessments of a notebook's readability.

We have assembled a dataset of approximately 160,000 public Python notebooks from Kaggle and have teamed up with [X, the moonshot factory](https://x.company/) to design a competition that challenges participants to use this dataset of published notebooks to build creative techniques aimed at better understanding the relationship between comment cells and code cells. 

![Image of Notebook Cells](https://storage.googleapis.com/kaggle-media/Images/notebook_cell_examples.png)

After the submission deadline, Kaggle and X will evaluate the performance of submitted techniques on new, previously unseen notebooks. We're excited to see how the insights learned from this competition affect the future of notebook authorship.