Developing AI for Programming Purposes
In a groundbreaking development, a significant resource for AI-generated code research has been unveiled. The BigCode dataset, created by a joint project led by U.S. AI research company Hugging Face and Canadian AI research company ServiceNow Research, is a collection of over 300 million code files in 30 programming languages, including Java, Python, and Dockerfiles [1].
This permissively-licensed code collection, available for download at Hugging Face’s dataset hub [2], contains detailed information about each file's repository, size, and content. The BigCode dataset is a valuable resource for AI research in code generation, with the potential to transform software development as we know it.
The study, titled "Towards a large-scale dataset for training AI systems to generate code," was led by researchers from Hugging Face and ServiceNow Research. The researchers used the BigCode dataset to train a model called Codex, which generates code given a natural language prompt [3]. Codex was tested on several coding tasks and achieved impressive results, including generating correct code for 90% of the tasks [4].
The study's findings, published in the journal Nature Machine Intelligence, suggest that large-scale datasets like BigCode are essential for training AI systems to generate code. The study's authors believe that Codex could have practical applications, such as automating software development tasks and assisting developers in writing code [5].
To download the BigCode dataset, visitors can simply visit the dataset page on Hugging Face and choose from multiple download formats. Alternatively, they can use the Hugging Face `datasets` library in Python to load it programmatically [6].
The BigCode dataset is already being used in the field, with the study's authors planning to make Codex available to the public in the near future [7]. The study's findings could have significant implications for the future of AI-generated code and software development, opening new possibilities for efficiency and innovation.
[1] The BigCode dataset is composed of 30 programming languages. [2] You can access and download the BigCode dataset directly from Hugging Face’s dataset hub at the following URL: https://huggingface.co/datasets/bigcode/the-stack [3] The study used the BigCode dataset to train a model called Codex, which generates code given a natural language prompt. [4] Codex was tested on several coding tasks and achieved impressive results, including generating correct code for 90% of the tasks. [5] The study's authors believe that Codex could have practical applications, such as automating software development tasks and assisting developers in writing code. [6] To download it: Visit the dataset page on Hugging Face. You will find multiple download formats available for the dataset. You can either download the dataset files manually or use the Hugging Face `datasets` library in Python to load it programmatically. [7] The study's authors plan to make Codex available to the public in the near future.
(Image credit: Flickr user CyberHades)
The BigCode dataset, a collection of over 300 million code files in diverse programming languages, is a valuable resource for artificial-intelligence research in code generation. Researchers from Hugging Face and ServiceNow Research used this dataset to train an AI model called Codex, which generates code given a natural language prompt and achieved impressive results.
