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--- |
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license: mit |
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datasets: |
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- Wannita/PyCoder |
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metrics: |
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- accuracy |
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- bleu |
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- meteor |
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- exact_match |
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- rouge |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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# PyCoder |
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This repository contains the model for the paper [Syntax-Aware On-the-Fly Code Completion](https://arxiv.org/abs/2211.04673) |
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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. |
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PyCoder is an auto code completion model which leverage a Multi-Task Training technique (MTT) to cooperatively |
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learn the code prediction task and the type prediction task. For the type prediction |
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task, we propose to leverage the standard Python token |
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type information (e.g., String, Number, Name, Keyword), |
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which is readily available and lightweight, instead of using |
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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). |
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More information can be found in our paper. |
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If you use our code or PyCoder, please cite our paper. |
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<pre><code>@article{takerngsaksiri2022syntax, |
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title={Syntax-Aware On-the-Fly Code Completion}, |
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author={Takerngsaksiri, Wannita and Tantithamthavorn, Chakkrit and Li, Yuan-Fang}, |
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journal={arXiv preprint arXiv:2211.04673}, |
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year={2022} |
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}</code></pre> |
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--- |
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license: mit |
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datasets: |
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- Wannita/PyCoder |
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metrics: |
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- accuracy |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
