|
"""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") |
|
|
|
|
|
|
|
|
|
|
|
|
