csatqa.py

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("float16"),
            }
        )
        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