| --- |
| 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 |
| --- |
