
# PyCoder

This repository contains the dataset 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 leverages 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>

task_categories:text-generation license:mit code arxiv:2211.04673 region:us
{"license": "mit", "task_categories": ["text-generation"], "datasets": ["Wannita/PyCoder"], "metrics": ["accuracy", "bleu", "meteor", "exact_match", "rouge"], "library_name": "transformers", "pipeline_tag": "text-generation", "tags": ["code"]}
TAGS
#task_categories-text-generation #license-mit #code #arxiv-2211.04673 #region-us 


# PyCoder

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

The sample code to run the model can be found in directory: "'assets/notebooks/URL'" in our GitHub: URL

PyCoder is an auto code completion model which leverages 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>

# PyCoder

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

The sample code to run the model can be found in directory: "'assets/notebooks/URL'" in our GitHub: URL

PyCoder is an auto code completion model which leverages 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>
TAGS
#task_categories-text-generation #license-mit #code #arxiv-2211.04673 #region-us 
 # PyCoder

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

The sample code to run the model can be found in directory: "'assets/notebooks/URL'" in our GitHub: URL

PyCoder is an auto code completion model which leverages 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>
passage: TAGS
#task_categories-text-generation #license-mit #code #arxiv-2211.04673 #region-us 
# PyCoder

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

The sample code to run the model can be found in directory: "'assets/notebooks/URL'" in our GitHub: URL

PyCoder is an auto code completion model which leverages 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>