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"""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 = """\ |
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TBD |
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""" |
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_DESCRIPTION = """\ |
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The dataset contains data for KoBEST dataset |
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""" |
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_URL = "https://github.com/SKT-LSL/KoBEST_datarepo" |
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_DATA_URLS = { |
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"boolq": { |
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"train": _URL + "/v1.0/BoolQ/train.tsv", |
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"dev": _URL + "/v1.0/BoolQ/dev.tsv", |
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"test": _URL + "/v1.0/BoolQ/test.tsv", |
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}, |
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"copa": { |
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"train": _URL + "/v1.0/COPA/train.tsv", |
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"dev": _URL + "/v1.0/COPA/dev.tsv", |
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"test": _URL + "/v1.0/COPA/test.tsv", |
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}, |
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"sentineg": { |
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"train": _URL + "/v1.0/SentiNeg/train.tsv", |
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"dev": _URL + "/v1.0/SentiNeg/dev.tsv", |
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"test": _URL + "/v1.0/SentiNeg/test.tsv", |
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}, |
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"hellaswag": { |
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"train": _URL + "/v1.0/HellaSwag/train.tsv", |
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"dev": _URL + "/v1.0/HellaSwag/dev.tsv", |
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"test": _URL + "/v1.0/HellaSwag/test.tsv", |
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}, |
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"wic": { |
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"train": _URL + "/v1.0/WiC/train.tsv", |
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"dev": _URL + "/v1.0/WiC/dev.tsv", |
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"test": _URL + "/v1.0/WiC/test.tsv", |
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}, |
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} |
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class KoBESTConfig(datasets.BuilderConfig): |
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"""Config for building KoBEST""" |
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def __init__(self, description, data_url, citation, url, **kwargs): |
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""" |
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Args: |
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description: `string`, brief description of the dataset |
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data_url: `dictionary`, dict with url for each split of data. |
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citation: `string`, citation for the dataset. |
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url: `string`, url for information about the dataset. |
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**kwrags: keyword arguments frowarded to super |
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""" |
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super(KoBESTConfig, self).__init__(version=datasets.Version("1.0", ""), **kwargs) |
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self.description = description |
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self.data_url = data_url |
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self.citation = citation |
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self.url = url |
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class KoBEST(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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KoBESTConfig(name=name, description=_DESCRIPTION, data_url=_DATA_URLS[name], citation=_CITATAION, url=_URL) |
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for name in ["boolq", "copa", 'sentineg', 'hellaswag', 'wic'] |
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] |
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BUILDER_CONFIG_CLASS = KoBESTConfig |
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def _info(self): |
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features = {} |
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if self.config.name == "boolq": |
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labels = ["True", "False"] |
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features["paragraph"] = datasets.Value("string") |
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features["question"] = datasets.Value("string") |
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features["label"] = datasets.features.ClassLabel(names=labels) |
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if self.config.name == "copa": |
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labels = ["alternative_1", "alternative_2"] |
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features["premise"] = datasets.Value("string") |
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features["question"] = datasets.Value("string") |
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features["alternative_1"] = datasets.Value("string") |
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features["alternative_2"] = datasets.Value("string") |
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features["label"] = datasets.features.ClassLabel(names=labels) |
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if self.config.name == "wic": |
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labels = ["True", "False"] |
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features["word"] = datasets.Value("string") |
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features["context_1"] = datasets.Value("string") |
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features["context_2"] = datasets.Value("string") |
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features["label"] = datasets.features.ClassLabel(names=labels) |
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if self.config.name == "hellaswag": |
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labels = ["ending_1", "ending_2", "ending_3", "ending_4"] |
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features["context"] = datasets.Value("string") |
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features["ending_1"] = datasets.Value("string") |
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features["ending_2"] = datasets.Value("string") |
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features["ending_3"] = datasets.Value("string") |
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features["ending_4"] = datasets.Value("string") |
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features["label"] = datasets.features.ClassLabel(names=labels) |
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if self.config.name == "sentineg": |
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labels = ["negative", "positive"] |
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features["sentence"] = datasets.Value("string") |
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features["label"] = datasets.features.ClassLabel(names=labels) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, features=datasets.Features(features), homepage=_URL, citation=_CITATAION |
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) |
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def _split_generators(self, dl_manager): |
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train = dl_manager.download_and_extract(self.config.data_url["train"]) |
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dev = dl_manager.download_and_extract(self.config.data_url["dev"]) |
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test = dl_manager.download_and_extract(self.config.data_url["test"]) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}), |
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] |
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def _generate_examples(self, filepath, split): |
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if self.config.name == "boolq": |
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df = pd.read_csv(filepath, sep="\t") |
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df = df.dropna() |
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for id_, row in df.iterrows(): |
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yield id_, { |
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"paragraph": str(row["Text"]), |
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"question": str(row["Question"]), |
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"label": str(int(row["Answer"])), |
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} |
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if self.config.name == "copa": |
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df = pd.read_csv(filepath, sep="\t") |
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df = df.dropna() |
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for id_, row in df.iterrows(): |
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yield id_, { |
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"premise": str(row["sentence"]), |
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"question": str(row["question"]), |
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"alternative_1": str(int(row["1"])), |
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"alternative_2": str(int(row["2"])), |
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"label": str(row["Answer"]-1), |
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} |
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if self.config.name == "wic": |
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df = pd.read_csv(filepath, sep="\t") |
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df = df.dropna() |
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for id_, row in df.iterrows(): |
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yield id_, { |
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"word": str(row["Target"]), |
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"context_1": str(row["SENTENCE1"]), |
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"context_2": str(int(row["SENTENCE2"])), |
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"label": str(int(row["Answer"])), |
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} |
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if self.config.name == "hellaswag": |
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df = pd.read_csv(filepath, sep="\t") |
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df = df.dropna() |
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for id_, row in df.iterrows(): |
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yield id_, { |
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"context": str(row["context"]), |
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"ending_1": str(row["choice1"]), |
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"ending_2": str(int(row["choice2"])), |
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"ending_3": str(int(row["choice3"])), |
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"ending_4": str(int(row["choice4"])), |
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"label": str(row["label"]), |
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} |
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if self.config.name == "sentineg": |
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df = pd.read_csv(filepath, sep="\t") |
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df = df.dropna() |
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for id_, row in df.iterrows(): |
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yield id_, { |
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"sentence": str(row["Text"]), |
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"label": str(int(row["Label"])), |
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} |
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