Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
100M<n<200M
Language Creators:
crowdsourced
Annotations Creators:
no-annotation
Source Datasets:
https://github.com/shibing624/code-autocomplete
https://github.com/bharathgs/Awesome-pytorch-list
https://github.com/akullpp/awesome-java
Tags:
License:
File size: 3,792 Bytes
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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": ""}
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