The present and future of semantic code search

- Its really large: Parallel corpus is 2M code snippets, and the unpaired corpus is 6M code snippets Its real data generated by people in the wild and gives everyone a chance to exercise skills with cleaning a messy dataset.

- Its unique: its [code, comment] pairs; part natural language, part code. This presents unique challenges. This data has seen very little utilization so far which means lots of opportunities for the community to make tools for code navigation, search, bug detection, automated documentation, and program synthesis (some of the problems being significantly harder than others).

- Other niche datasets from specific domains require a non-trivial amount of domain knowledge before being effective with the dataset. For example, to utilize radiology images it is extremely helpful to learn a little about the domain to help with cleaning and exploring the data and debugging your models. However, many machine learning folks are already familiar with writing code so you have a head start!

- The most painful part of cleaning the data is done for you: parsing the code and separating out the comments from the code.

We are really excited about the possibilities of this dataset and what folks are able to do with it. Looking forward to your questions.