README.md

metadata

annotations_creators:
  - no-annotation
language_creators:
  - crowdsourced
languages:
  - en
licenses:
  - cc-by-4-0
  - gfdl-1-3-or-later
multilinguality:
  - monolingual
size_categories:
  - 100M<n<200M
source_datasets:
  - https://github.com/shibing624/code-autocomplete
  - https://github.com/bharathgs/Awesome-pytorch-list
  - https://github.com/akullpp/awesome-java
  - https://github.com/fffaraz/awesome-cpp
task_categories:
  - sequence-modeling
task_ids:
  - language-modeling

Dataset Card for "SourceCode"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Repository: code-autocomplete
• Leaderboard: leaderboard (located on the homepage)
• Size of downloaded dataset files: 105 MB
• Total amount of disk used: 570 MB

Dataset Summary

Source code dataset is a collection of Github awesome repos, it contains Python, Java, C++, and other programming languages. This dataset can be used in different NLP tasks like language modeling and text generation tasks.

PYTHON_CODE_HOME = "https://github.com/bharathgs/Awesome-pytorch-list"
JAVA_CODE_HOME = "https://github.com/akullpp/awesome-java"
CPP_CODE_HOME = "https://github.com/fffaraz/awesome-cpp"

Supported Tasks and Leaderboards

• language modeling
• code generation tasks: Leaderboard: code-autocomplete

Languages

• programming languages: Python, Java, C++
• natural language: English

Dataset Structure

Data Instances

An example of 'train' looks as follows.

This example was too long and was cropped:

{
    "text": """
import json
import argparse


def _parse_args():
    parser = argparse.ArgumentParser(
        description=__doc__,
        formatter_class=argparse.RawTextHelpFormatter,
    )
    parser.add_argument(
        '--model-file',
        required=True,
        help=(
            'A pt file from '
            'https://github.com/pytorch/fairseq/tree/main/examples/hubert'
        )
    )
    return parser.parse_args()
    """
}

Data Fields

The data fields are the same among all splits.

• text: a string feature.

Data Splits

python

$ wc -l python/*
   10000 python/test.txt
 5215412 python/train.txt
   10000 python/valid.txt
 5235412 total

java

$ wc -l java/*  
  950083 java/test.txt
 2802880 java/train.txt
  940803 java/valid.txt
 4693766 total

cpp

$ wc -l cpp/* 
 1060014 cpp/test.txt
 3119241 cpp/train.txt
 1099124 cpp/valid.txt
 5278379 total

Dataset Creation

Curation Rationale

As code generation dataset, I upload it to huggingface datasets.

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Citation:

APA:

Xu, M. code-autocomplete: Code AutoComplete with GPT2 model (Version 0.0.4) [Computer software]. https://github.com/shibing624/code-autocomplete

BibTeX:

@software{Xu_code-autocomplete_Code_AutoComplete,
author = {Xu, Ming},
title = {code-autocomplete: Code AutoComplete with GPT2 model},
url = {https://github.com/shibing624/code-autocomplete},
version = {0.0.4}
}

Annotations

Annotation process

Who are the annotators?

nobody

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

This dataset was developed as a benchmark for evaluating code generation model.

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Github awesome programing code repos.

Licensing Information

For research use only.

Contributions

Thanks to @shibing624 add this dataset.