Most code models are trained on data from public software repositories hosted on GitHub. Some also include code coupled with natural text from Stackoverflow for example. Additional datasets can be crafted based on the target task of the model. [Alphacode](https://arxiv.org/pdf/2203.07814v1.pdf), for instance, was fine-tuned on [CodeContests](https://github.com/deepmind/code_contests), a competitive programming dataset for machine-learning. Another popular dataset is [The Pile](https://huggingface.co/datasets/the_pile), it is a large corpus containing both natural language texts and code from different sources such as StackExchange dumps and popular (>100 stars) GitHub repositories. It can be efficient for models intended to do translation from natural text to code or the opposite, it was used in [CodeGen](https://arxiv.org/pdf/2203.13474.pdf) for instance. 

Some other useful datasets that are available on the 🤗 hub are [CodeSearchNet](https://huggingface.co/datasets/code_search_net), a corpus of 2 milllion (comment, code) pairs from opensource libraries hosted on GitHub for several programming languages, and [Mostly Basic Python Problems (mbpp)](https://huggingface.co/datasets/mbpp), a benchmark of around 1,000 crowd-sourced Python programming problems, for entry level programmers, where each problem consists of a task description, code solution and 3 automated test cases, this dataset was used in [InCoder](https://huggingface.co/facebook/incoder-6B) evaluation in addition to [HumanEval](https://huggingface.co/datasets/openai_humaneval) that we will present later.