Datasets:
Tasks:
Text Classification
Languages:
Korean
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Annotations Creators:
found
Source Datasets:
extended|snli
ArXiv:
Tags:
License:
"""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"], | |
} | |