Datasets:
Languages:
Korean
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
| """Korean Balanced Evaluation of Significant Tasks""" | |
| import csv | |
| 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__": | |
| dataset = datasets.load_dataset("kobest_v1.py", 'sentineg', ignore_verifications=True) | |
| ds = dataset['test_originated'] | |
| print(ds) | |
| # 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']) | |