| import os | |
| import datasets | |
| import json | |
| import pandas as pd | |
| _CITATION = """\ | |
| """ | |
| _DESCRIPTION = """\ | |
| CSAT-QA | |
| """ | |
| _HOMEPAGE = "https://huggingface.co/HAERAE-HUB" | |
| _LICENSE = "Proprietary" | |
| split_names = ["full","WR", "GR", "RCS", "RCSS", "RCH", "LI"] | |
| class CSATQAConfig(datasets.BuilderConfig): | |
| def __init__(self, **kwargs): | |
| super().__init__(version=datasets.Version("1.0.0"), **kwargs) | |
| class CSATQA(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| CSATQAConfig( | |
| name=name, | |
| ) | |
| for name in split_names | |
| ] | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "question": datasets.Value("string"), | |
| "context" : datasets.Value("string"), | |
| "option#1": datasets.Value("string"), | |
| "option#2": datasets.Value("string"), | |
| "option#3": datasets.Value("string"), | |
| "option#4": datasets.Value("string"), | |
| "option#5": datasets.Value("string"), | |
| "gold": datasets.Value("int8"), | |
| "category": datasets.Value("string"), | |
| "human_performance": datasets.Value("float32"), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| train_path = dl_manager.download_and_extract("./data/csatqa.json") | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": train_path, | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath): | |
| with open(filepath, encoding="utf-8") as f: | |
| buffer = [] | |
| for key, row in enumerate(f): | |
| data = json.loads(row) | |
| if self.config.name == "full": | |
| buffer.append({ | |
| "question": data["question"], | |
| "context" : data["context"], | |
| "option#1": data["option#1"], | |
| "option#2": data["option#2"], | |
| "option#3": data["option#3"], | |
| "option#4": data["option#4"], | |
| "option#5": data["option#5"], | |
| "gold": data["gold"], | |
| "category":"N/A", | |
| "human_performance":0.0 | |
| }) | |
| elif data["Category"] == self.config.name: | |
| buffer.append({ | |
| "question": data["question"], | |
| "context" : data["context"], | |
| "option#1": data["option#1"], | |
| "option#2": data["option#2"], | |
| "option#3": data["option#3"], | |
| "option#4": data["option#4"], | |
| "option#5": data["option#5"], | |
| "gold": data["gold"], | |
| "category": data["Category"], | |
| "human_performance": data["Human_Peformance"] | |
| }) | |
| for idx, dat in enumerate(buffer): | |
| yield idx,dat | |