Annoy-PyEdu-Rs-Raw

Annoy: This should be a paper Title

<p align="left"> 📑 <a href="https://huggingface.co/papers/xxxx.xxxxx" target="_blank">Paper</a> &nbsp&nbsp | &nbsp&nbsp 🌐 <a href="https://specx.github.io/" target="_blank">Project Page</a> &nbsp&nbsp | &nbsp&nbsp 💾 <a href="https://huggingface.co/collections/mm-tool/specx-67a978e28fd926b56a4f55a2" target="_blank">Released Resources</a> &nbsp&nbsp | &nbsp&nbsp 📦 <a href="https://github.com/mcpllmbench-ops/Annoy" target="_blank">Repo</a>

We release the raw data for our processed PythonEdu-Rs dataset, adopted from the original dataset from HuggingFaceTB team.

The data format for each line in the 0_368500_filtered_v2_ds25.sced.jsonl is as follows:

{
  "problem_description": <the problem description of the function>,
  "io_requirements": <the input/output requirements and constraints>,
  "refcode": <the reference code, including imported packages (optional), auxiliary functions (optional) and main entrypoint function>,
  "funcname": <the function name for the entrypoint function>,
  "ios": [
    {
      "input": <the input arguments>,
      "output":<the returned value>
    },
    ...
  ],
  "source": <the source of the raw code files>,
  "category": <the reasoning type we assign to this sample>,
  "meta": <meta information about this sample>
}

Some of the ios are empty. The reason is that when executing the code, the input/output sizes are too large and exceed our required constraints. Thus, they are not stored or used later.

*Note: Due to imperfect LLM-based transformations, some problem descriptions do not contain enough information to describe the code. We leave this as future work to further enhance our data and update it to a better version.

License

The license for this dataset is CC-BY-4.0.

License

The license for this dataset is The Stack v2 License.

License

The license for this dataset is Apache 2.0.