Datasets:

taishi-i
/

awesome-japanese-nlp-classification-dataset

	import json

	import datasets

	_DESCRIPTION = """\
	This dataset determines whether a GitHub repository description relates to Japanese natural language processing (NLP). The labels are categorized as "Relevant (1)" and "Not Relevant (0)".
	"""

	_HOMEPAGE = "https://github.com/taishi-i/awesome-japanese-nlp-resources"
	_CITATION = ""
	_LICENSE = "other"


	class NagisaStopwordsDataset(datasets.GeneratorBasedBuilder):
	"""awesome-japanese-nlp-classification-dataset."""

	VERSION = datasets.Version("0.0.1")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="awesome-japanese-nlp-classification-dataset",
	version=VERSION,
	description=_DESCRIPTION,
	),
	]

	def _info(self):
	features = datasets.Features(
	{
	"label": datasets.features.ClassLabel(names=["0", "1"]),
	"text": datasets.Value("string"),
	"url": datasets.Value("string"),
	"created_at": datasets.Value("string"),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_url = "https://huggingface.co/datasets/taishi-i/awesome-japanese-nlp-classification-dataset/raw/main"

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": dl_manager.download_and_extract(
	f"{data_url}/train.json"
	),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": dl_manager.download_and_extract(
	f"{data_url}/val.json"
	),
	"split": "val",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": dl_manager.download_and_extract(
	f"{data_url}/test.json"
	),
	"split": "test",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""Generates examples."""
	with open(filepath, "r") as file:
	data = json.load(file)
	for id_, row in enumerate(data):
	yield id_, {
	"label": row["label"],
	"text": row["text"],
	"url": row["url"],
	"created_at": row["created_at"],
	}