Datasets:

kakaobrain
/

kor_nlu

	"""Korean Dataset for NLI and STS"""


	import csv

	import pandas as pd

	import datasets


	_CITATAION = """\
	@article{ham2020kornli,
	title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
	author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
	journal={arXiv preprint arXiv:2004.03289},
	year={2020}
	}
	"""

	_DESCRIPTION = """\
	The dataset contains data for bechmarking korean models on NLI and STS
	"""

	_URL = "https://github.com/kakaobrain/KorNLUDatasets"

	_DATA_URLS = {
	"nli": {
	# 'mnli-train': 'https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/multinli.train.ko.tsv',
	"snli-train": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/snli_1.0_train.ko.tsv",
	"xnli-dev": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/xnli.dev.ko.tsv",
	"xnli-test": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorNLI/xnli.test.ko.tsv",
	},
	"sts": {
	"train": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorSTS/sts-train.tsv",
	"dev": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorSTS/sts-dev.tsv",
	"test": "https://raw.githubusercontent.com/kakaobrain/KorNLUDatasets/master/KorSTS/sts-test.tsv",
	},
	}


	class KorNluConfig(datasets.BuilderConfig):
	"""BuilderConfig for korNLU"""

	def __init__(self, description, data_url, citation, url, **kwargs):
	"""
	Args:
	description: `string`, brief description of the dataset
	data_url: `dictionary`, dict with url for each split of data.
	citation: `string`, citation for the dataset.
	url: `string`, url for information about the dataset.
	**kwrags: keyword arguments frowarded to super
	"""
	super(KorNluConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
	self.description = description
	self.data_url = data_url
	self.citation = citation
	self.url = url


	class KorNlu(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	KorNluConfig(name=name, description=_DESCRIPTION, data_url=_DATA_URLS[name], citation=_CITATAION, url=_URL)
	for name in ["nli", "sts"]
	]
	BUILDER_CONFIG_CLASS = KorNluConfig

	def _info(self):
	features = {}
	if self.config.name == "nli":
	labels = ["entailment", "neutral", "contradiction"]
	features["premise"] = datasets.Value("string")
	features["hypothesis"] = datasets.Value("string")
	features["label"] = datasets.features.ClassLabel(names=labels)

	if self.config.name == "sts":
	genre = ["main-news", "main-captions", "main-forum", "main-forums"]
	filename = [
	"images",
	"MSRpar",
	"MSRvid",
	"headlines",
	"deft-forum",
	"deft-news",
	"track5.en-en",
	"answers-forums",
	"answer-answer",
	]
	year = ["2017", "2016", "2013", "2012train", "2014", "2015", "2012test"]

	features["genre"] = datasets.features.ClassLabel(names=genre)
	features["filename"] = datasets.features.ClassLabel(names=filename)
	features["year"] = datasets.features.ClassLabel(names=year)
	features["id"] = datasets.Value("int32")
	features["score"] = datasets.Value("float32")
	features["sentence1"] = datasets.Value("string")
	features["sentence2"] = datasets.Value("string")

	return datasets.DatasetInfo(
	description=_DESCRIPTION, features=datasets.Features(features), homepage=_URL, citation=_CITATAION
	)

	def _split_generators(self, dl_manager):
	if self.config.name == "nli":
	# mnli_train = dl_manager.download_and_extract(self.config.data_url['mnli-train'])
	snli_train = dl_manager.download_and_extract(self.config.data_url["snli-train"])
	xnli_dev = dl_manager.download_and_extract(self.config.data_url["xnli-dev"])
	xnli_test = dl_manager.download_and_extract(self.config.data_url["xnli-test"])

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN, gen_kwargs={"filepath": snli_train, "split": "train"}
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION, gen_kwargs={"filepath": xnli_dev, "split": "dev"}
	),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": xnli_test, "split": "test"}),
	]

	if self.config.name == "sts":
	train = dl_manager.download_and_extract(self.config.data_url["train"])
	dev = dl_manager.download_and_extract(self.config.data_url["dev"])
	test = dl_manager.download_and_extract(self.config.data_url["test"])

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
	]

	def _generate_examples(self, filepath, split):
	if self.config.name == "nli":
	df = pd.read_csv(filepath, sep="\t")
	df = df.dropna()
	for id_, row in df.iterrows():
	yield id_, {
	"premise": str(row["sentence1"]),
	"hypothesis": str(row["sentence2"]),
	"label": str(row["gold_label"]),
	}

	if self.config.name == "sts":
	with open(filepath, encoding="utf-8") as f:
	data = csv.DictReader(f, delimiter="\t")
	for id_, row in enumerate(data):
	yield id_, {
	"genre": row["genre"],
	"filename": row["filename"],
	"year": row["year"],
	"id": row["id"],
	"sentence1": row["sentence1"],
	"sentence2": row["sentence2"],
	"score": row["score"],
	}