PyCoder

This repository contains the model for the paper Syntax-Aware On-the-Fly Code Completion

The sample code to run the model can be found in directory: "assets/notebooks/inference.ipynb" in our GitHub: https://github.com/awsm-research/pycoder.

PyCoder is an auto code completion model which leverage a Multi-Task Training technique (MTT) to cooperatively learn the code prediction task and the type prediction task. For the type prediction task, we propose to leverage the standard Python token type information (e.g., String, Number, Name, Keyword), which is readily available and lightweight, instead of using the AST information which requires source code to be parsable for an extraction, limiting its ability to perform on-the-fly code completion (see Section 2.3 in our paper).

More information can be found in our paper.

If you use our code or PyCoder, please cite our paper.

@article{takerngsaksiri2022syntax,
  title={Syntax-Aware On-the-Fly Code Completion},
  author={Takerngsaksiri, Wannita and Tantithamthavorn, Chakkrit and Li, Yuan-Fang},
  journal={arXiv preprint arXiv:2211.04673},
  year={2022}
}

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license: mit datasets: - Wannita/PyCoder metrics: - accuracy library_name: transformers pipeline_tag: text-generation