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"""Korean Balanced Evaluation of Significant Tasks"""
import csv
import os
import pandas as pd
import datasets
_CITATAION = """\
@misc{https://doi.org/10.48550/arxiv.2204.04541,
doi = {10.48550/ARXIV.2204.04541},
url = {https://arxiv.org/abs/2204.04541},
author = {Kim, Dohyeong and Jang, Myeongjun and Kwon, Deuk Sin and Davis, Eric},
title = {KOBEST: Korean Balanced Evaluation of Significant Tasks},
publisher = {arXiv},
year = {2022},
}
"""
_DESCRIPTION = """\
The dataset contains data for KoBEST dataset
"""
_URL = "https://github.com/SKT-LSL/KoBEST_datarepo/raw/main"
_DATA_URLS = {
"boolq": {
"train": _URL + "/v1.0/BoolQ/train.tsv",
"dev": _URL + "/v1.0/BoolQ/dev.tsv",
"test": _URL + "/v1.0/BoolQ/test.tsv",
},
"copa": {
"train": _URL + "/v1.0/COPA/train.tsv",
"dev": _URL + "/v1.0/COPA/dev.tsv",
"test": _URL + "/v1.0/COPA/test.tsv",
},
"sentineg": {
"train": _URL + "/v1.0/SentiNeg/train.tsv",
"dev": _URL + "/v1.0/SentiNeg/dev.tsv",
"test": _URL + "/v1.0/SentiNeg/test.tsv",
"test_originated": _URL + "/v1.0/SentiNeg/test.tsv",
},
"hellaswag": {
"train": _URL + "/v1.0/HellaSwag/train.tsv",
"dev": _URL + "/v1.0/HellaSwag/dev.tsv",
"test": _URL + "/v1.0/HellaSwag/test.tsv",
},
"wic": {
"train": _URL + "/v1.0/WiC/train.tsv",
"dev": _URL + "/v1.0/WiC/dev.tsv",
"test": _URL + "/v1.0/WiC/test.tsv",
},
}
_LICENSE = "CC-BY-SA-4.0"
class KoBESTConfig(datasets.BuilderConfig):
"""Config for building KoBEST"""
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(KoBESTConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.description = description
self.data_url = data_url
self.citation = citation
self.url = url
class KoBEST(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
KoBESTConfig(name=name, description=_DESCRIPTION, data_url=_DATA_URLS[name], citation=_CITATAION, url=_URL)
for name in ["boolq", "copa", 'sentineg', 'hellaswag', 'wic']
]
BUILDER_CONFIG_CLASS = KoBESTConfig
def _info(self):
features = {}
if self.config.name == "boolq":
labels = ["False", "True"]
features["paragraph"] = datasets.Value("string")
features["question"] = datasets.Value("string")
features["label"] = datasets.features.ClassLabel(names=labels)
if self.config.name == "copa":
labels = ["alternative_1", "alternative_2"]
features["premise"] = datasets.Value("string")
features["question"] = datasets.Value("string")
features["alternative_1"] = datasets.Value("string")
features["alternative_2"] = datasets.Value("string")
features["label"] = datasets.features.ClassLabel(names=labels)
if self.config.name == "wic":
labels = ["False", "True"]
features["word"] = datasets.Value("string")
features["context_1"] = datasets.Value("string")
features["context_2"] = datasets.Value("string")
features["label"] = datasets.features.ClassLabel(names=labels)
if self.config.name == "hellaswag":
labels = ["ending_1", "ending_2", "ending_3", "ending_4"]
features["context"] = datasets.Value("string")
features["ending_1"] = datasets.Value("string")
features["ending_2"] = datasets.Value("string")
features["ending_3"] = datasets.Value("string")
features["ending_4"] = datasets.Value("string")
features["label"] = datasets.features.ClassLabel(names=labels)
if self.config.name == "sentineg":
labels = ["negative", "positive"]
features["sentence"] = datasets.Value("string")
features["label"] = datasets.features.ClassLabel(names=labels)
return datasets.DatasetInfo(
description=_DESCRIPTION, features=datasets.Features(features), homepage=_URL, citation=_CITATAION
)
def _split_generators(self, dl_manager):
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"])
if self.config.data_url.get("test_originated"):
test_originated = dl_manager.download_and_extract(self.config.data_url["test_originated"])
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"}),
datasets.SplitGenerator(name="test_originated", gen_kwargs={"filepath": test_originated, "split": "test_originated"}),
]
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 == "boolq":
df = pd.read_csv(filepath, sep="\t")
df = df.dropna()
df = df[['Text', 'Question', 'Answer']]
df = df.rename(columns={
'Text': 'paragraph',
'Question': 'question',
'Answer': 'label',
})
df['label'] = [0 if str(s) == 'False' else 1 for s in df['label'].tolist()]
elif self.config.name == "copa":
df = pd.read_csv(filepath, sep="\t")
df = df.dropna()
df = df[['sentence', 'question', '1', '2', 'Answer']]
df = df.rename(columns={
'sentence': 'premise',
'question': 'question',
'1': 'alternative_1',
'2': 'alternative_2',
'Answer': 'label',
})
df['label'] = [i-1 for i in df['label'].tolist()]
elif self.config.name == "wic":
df = pd.read_csv(filepath, sep="\t")
df = df.dropna()
df = df[['Target', 'SENTENCE1', 'SENTENCE2', 'ANSWER']]
df = df.rename(columns={
'Target': 'word',
'SENTENCE1': 'context_1',
'SENTENCE2': 'context_2',
'ANSWER': 'label',
})
df['label'] = [0 if str(s) == 'False' else 1 for s in df['label'].tolist()]
elif self.config.name == "hellaswag":
df = pd.read_csv(filepath, sep="\t")
df = df.dropna()
df = df[['context', 'choice1', 'choice2', 'choice3', 'choice4', 'label']]
df = df.rename(columns={
'context': 'context',
'choice1': 'ending_1',
'choice2': 'ending_2',
'choice3': 'ending_3',
'choice4': 'ending_4',
'label': 'label',
})
elif self.config.name == "sentineg":
df = pd.read_csv(filepath, sep="\t")
df = df.dropna()
if split == "test_originated":
df = df[['Text_origin', 'Label_origin']]
df = df.rename(columns={
'Text_origin': 'sentence',
'Label_origin': 'label',
})
else:
df = df[['Text', 'Label']]
df = df.rename(columns={
'Text': 'sentence',
'Label': 'label',
})
else:
raise NotImplementedError
for id_, row in df.iterrows():
features = {key: row[key] for key in row.keys()}
yield id_, features
if __name__ == "__main__":
for config_name in ["boolq", "copa", 'sentineg', 'hellaswag', 'wic']:
dataset = datasets.load_dataset("kobest_v1.py", config_name, ignore_verifications=True)
os.makedirs(config_name, exist_ok=True)
for split, split_dataset in dataset.items():
split_dataset.to_json(f"{config_name}/{split}.jsonl")
# for task in ['boolq', 'copa', 'wic', 'hellaswag', 'sentineg']:
# dataset = datasets.load_dataset("kobest_v1.py", task, ignore_verifications=True)
# print(dataset)
# print(dataset['train']['label'])
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