Hello folks, this is Hamel from GitHub -- I’m one of the Machine Learning Engineers who worked on this project. The reason we are excited to host this data is that we believe the community will be able to innovate and advance the state of the art much faster if it is provided in a tractable format for machine learning researchers. This data is already public, however there is specialized knowledge required to acquire, parse, dedupe, and clean code from many programming languages at a massive scale on GitHub. We strived to reduce these barriers to encourage greater involvement.
While we present the task of information retrieval as one possible use case of this dataset, we know there could be other practical applications of this data (i.e. code summarization). While we went through great lengths to pre-process the data for the community, the data is still messy and often you will find that there might not be high-quality comments that are aligned with a code snippet we parsed. However, we believe this is part of the excitement of the dataset it poses challenges that machine learning practitioners will have to address.
Code is very different than natural language with regard to its structure and syntactic rules and may benefit from different approaches relative to natural language processing. Our baseline models and benchmarks mostly treat code as natural language, however we are aware that there could be an opportunity to innovate on this front. If anyone creates any interesting projects from this dataset, please do get in touch. Happy to answer any questions!
While we present the task of information retrieval as one possible use case of this dataset, we know there could be other practical applications of this data (i.e. code summarization). While we went through great lengths to pre-process the data for the community, the data is still messy and often you will find that there might not be high-quality comments that are aligned with a code snippet we parsed. However, we believe this is part of the excitement of the dataset it poses challenges that machine learning practitioners will have to address.
Code is very different than natural language with regard to its structure and syntactic rules and may benefit from different approaches relative to natural language processing. Our baseline models and benchmarks mostly treat code as natural language, however we are aware that there could be an opportunity to innovate on this front. If anyone creates any interesting projects from this dataset, please do get in touch. Happy to answer any questions!