| """Korean Balanced Evaluation of Significant Tasks""" |
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|
| import csv |
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| import pandas as pd |
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| import datasets |
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| _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" |
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|
| _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" |
|
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|
|
| 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 |
|
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|
|
| if __name__ == "__main__": |
| dataset = datasets.load_dataset("kobest_v1.py", 'sentineg', ignore_verifications=True) |
| ds = dataset['test_originated'] |
| print(ds) |
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