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from __future__ import annotations
import random
from pathlib import Path
from typing import Generator
import datasets
_CITATION = ""
_DESCRIPTION = "This is a dataset of livedoor news articles."
_HOMEPAGE = "https://www.rondhuit.com/download.html#news%20corpus"
_LICENSE = "This work is license under CC BY-ND 2.1 JP"
_URL = "https://www.rondhuit.com/download/ldcc-20140209.tar.gz"
class LivedoorNewsCorpusConfig(datasets.BuilderConfig):
def __init__(
self,
name: str = "default",
version: datasets.Version | str | None = datasets.Version("0.0.0"),
data_dir: str | None = None,
data_files: datasets.data_files.DataFilesDict | None = None,
description: str | None = _DESCRIPTION,
shuffle: bool = True,
seed: int = 42,
train_ratio: float = 0.8,
validation_ratio: float = 0.1,
) -> None:
super().__init__(
name=name,
version=version,
data_dir=data_dir,
data_files=data_files,
description=description,
)
self.shuffle = shuffle
self.seed = seed
self.train_ratio = train_ratio
self.validation_ratio = validation_ratio
class LivedoorNewsCorpus(datasets.GeneratorBasedBuilder):
BUILDER_CONFIG_CLASS = LivedoorNewsCorpusConfig
def _info(self) -> datasets.DatasetInfo:
return datasets.DatasetInfo(
description=_DESCRIPTION,
citation=_CITATION,
homepage=_HOMEPAGE,
license=_LICENSE,
features=datasets.Features(
{
"url": datasets.Value("string"),
"date": datasets.Value("string"),
"title": datasets.Value("string"),
"content": datasets.Value("string"),
"category": datasets.Value("string"),
}
),
)
def _split_generators(
self, dl_manager: datasets.DownloadManager
) -> list[datasets.SplitGenerator]:
dataset_dir = Path(dl_manager.download_and_extract(_URL))
data = []
for file_name in sorted(dataset_dir.glob("*/*/*")):
if "LICENSE.txt" in str(file_name):
continue
with open(file_name, "r", encoding="utf-8") as f:
d = [line.strip() for line in f]
data.append(
{
"url": d[0],
"date": d[1],
"title": d[2],
"content": " ".join(d[3:]),
"category": file_name.parent.name,
}
)
if self.config.shuffle:
random.seed(self.config.seed)
random.shuffle(data)
num_data = len(data)
num_train_data = int(num_data * self.config.train_ratio)
num_validation_data = int(num_data * self.config.validation_ratio)
train_data = data[:num_train_data]
validation_data = data[num_train_data : num_train_data + num_validation_data]
test_data = data[num_train_data + num_validation_data :]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"data": train_data}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"data": validation_data}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"data": test_data}
),
]
def _generate_examples(self, data: list[dict[str, str]]) -> Generator:
for i, d in enumerate(data):
yield i, d
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