I came across this dataset recently, a collection of 22k Python code examples, tested and verified to work. What really caught my attention is how this was put together—they used a custom script to extract Python code from Alpaca-formatted datasets, tested each snippet locally, and only kept the functional ones. Non-functional examples were separated into their own file.

The dataset pulls from a mix of open-source projects like Wizard-LM’s Evol datasets, CodeUp’s 19k, and a bunch of others, plus some hand-prompted GPT-4 examples. Everything’s been deduplicated, so you’re not stuck with repeats.

It’s especially cool if you’re working on training AI models for coding tasks because it sidesteps one of the biggest issues with open datasets: non-functional or broken code. They even hinted at adapting the script for other languages like C++ or SQL.

If you use the dataset or their script, they ask for attribution: Filtered Using Vezora’s CodeTester. Oh, and they’re working on releasing an even bigger dataset with 220,000+ examples, definitely one to keep an eye on!

On Huggingface: Tested-22k-Python-Alpaca

Read also how to analyze a dataset.