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README.md

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----
-annotations_creators:
-- no-annotation
-language_creators:
-- crowdsourced
-language:
-- en
-license:
-- cc-by-4.0
-- gfdl
-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:
-- text-generation
-task_ids:
-- language-modeling
----
-# Dataset Card for "SourceCode"
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-- **Repository:** [code-autocomplete](https://github.com/shibing624/code-autocomplete)
-- **Leaderboard:** [leaderboard](https://github.com/shibing624/code-autocomplete) (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.
-
-data source:
-
-- PYTHON_CODE: https://github.com/bharathgs/Awesome-pytorch-list
-- JAVA_CODE: https://github.com/akullpp/awesome-java
-- CPP_CODE: https://github.com/fffaraz/awesome-cpp
-
-
-### Supported Tasks and Leaderboards
-- language modeling 
-- code generation tasks, **Leaderboard:** [code-autocomplete](https://github.com/shibing624/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
-```shell
-$ wc -l python/*
-   10000 python/test.txt
- 5215412 python/train.txt
-   10000 python/valid.txt
- 5235412 total
-```
-#### java
-```shell
-$ wc -l java/*  
-  950083 java/test.txt
- 2802880 java/train.txt
-  940803 java/valid.txt
- 4693766 total
-```
-#### cpp
-```shell
-$ 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:
-```latex
-Xu, M. code-autocomplete: Code AutoComplete with GPT2 model (Version 0.0.4) [Computer software]. https://github.com/shibing624/code-autocomplete
-```
-
-BibTeX:
-```latex
-@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
-
-GNU Free Documentation License v1.3 or later.
-
-For research use only.
-
-### Contributions
-Thanks to [@shibing624](https://github.com/shibing624) add this dataset.

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source_code.py

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-# -*- coding: utf-8 -*-
-"""
-@author:XuMing(xuming624@qq.com)
-@description:
-"""
-
-"""Code AutoComplete Python dataset Corpus.(code_autocomplete)"""
-
-import os
-
-import datasets
-
-_DESCRIPTION = """纯文本数据，内容：高质量编程源代码，包括Python，Java，CPP源代码"""
-
-PYTHON_HOME = "https://github.com/bharathgs/Awesome-pytorch-list"
-JAVA_HOME = "https://github.com/akullpp/awesome-java"
-CPP_HOME = "https://github.com/fffaraz/awesome-cpp"
-
-_CITATION = "https://github.com/shibing624/code-autocomplete"
-
-_DATA_URL = "https://github.com/shibing624/code-autocomplete/releases/download/0.0.4/source_code.zip"
-
-
-class SourceCodeConfig(datasets.BuilderConfig):
-    """BuilderConfig for NLI_zh"""
-
-    def __init__(self, features, data_url, citation, url, **kwargs):
-        """BuilderConfig for NLI_zh
-        Args:
-          features: `list[string]`, list of the features that will appear in the
-            feature dict. Should not include "label".
-          data_url: `string`, url to download the zip file from.
-          citation: `string`, citation for the data set.
-          url: `string`, url for information about the data set.
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
-        self.features = features
-        self.data_url = data_url
-        self.citation = citation
-        self.url = url
-
-
-class SourceCode(datasets.GeneratorBasedBuilder):
-    """The Natural Language Inference Chinese(NLI_zh) Corpus."""
-
-    BUILDER_CONFIGS = [
-        SourceCodeConfig(
-            name="python",
-            description=_DESCRIPTION,
-            features=["text"],
-            data_url=_DATA_URL,
-            citation=_CITATION,
-            url=PYTHON_HOME,
-        ),
-        SourceCodeConfig(
-            name="java",
-            description=_DESCRIPTION,
-            features=["text"],
-            data_url=_DATA_URL,
-            citation=_CITATION,
-            url=JAVA_HOME,
-        ),
-        SourceCodeConfig(
-            name="cpp",
-            description=_DESCRIPTION,
-            features=["text"],
-            data_url=_DATA_URL,
-            citation=_CITATION,
-            url=CPP_HOME,
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=self.config.description,
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                }
-            ),
-            homepage=self.config.url,
-            citation=self.config.citation,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
-        dl_dir = os.path.join(dl_dir, f"source_code/{self.config.name}")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(dl_dir, f"train.txt"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(dl_dir, f"valid.txt"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(dl_dir, f"test.txt"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        with open(filepath, 'r', encoding="utf-8") as f:
-            for idx, row in enumerate(f):
-                if row.strip():
-                    yield idx, {"text": row}
-                else:
-                    yield idx, {"text": ""}