|
|
|
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 |
|
|
|
|
