| | --- |
| | license: mit |
| | datasets: |
| | - Wannita/PyCoder |
| | - Wannita/PyCoder-Type |
| | metrics: |
| | - accuracy |
| | - bleu |
| | - meteor |
| | - exact_match |
| | - rouge |
| | library_name: transformers |
| | pipeline_tag: text-generation |
| | tags: |
| | - code |
| | - code completion |
| | --- |
| | # PyCoder |
| |
|
| | This repository contains the model for the paper [Syntax-Aware On-the-Fly Code Completion](https://arxiv.org/abs/2211.04673) |
| |
|
| | 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. |
| |
|
| | <pre><code>@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} |
| | }</code></pre> |
| |
|
| | --- |
| | license: mit |
| | datasets: |
| | - Wannita/PyCoder |
| | metrics: |
| | - accuracy |
| | library_name: transformers |
| | pipeline_tag: text-generation |
| | --- |
