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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import datasets
import pandas as pd
_CITATION = """\
"""
_DESCRIPTION = """\
"""
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
domains = {
"Human Science": [
"Human Science",
"Art & Architecture",
],
"Medicine": [
"Medical Science",
"Veterinary Science",
],
"Science & engineering": [
"Agriculture & Natural Resources",
"Engineering & Technology",
"Basic Science",
],
}
class ESPOSITOConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(version=datasets.Version("1.0.0"), **kwargs)
class ESPOSITO(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
ESPOSITOConfig(
name=domain,
)
for domain in domains.keys()
]
def _info(self):
features = datasets.Features(
{
"id":datasets.Value("int32"),
"question": datasets.Value("string"),
"A": datasets.Value("string"),
"B": datasets.Value("string"),
"C": datasets.Value("string"),
"D": datasets.Value("string"),
"answer": datasets.Value("string"),
"explanation":datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URL)
task_name = self.config.name
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(
data_dir, "test", f"{task_name}_test.csv"
),
},
),
datasets.SplitGenerator(
name=datasets.Split("val"),
gen_kwargs={
"filepath": os.path.join(
data_dir, "val", f"{task_name}_val.csv"
),
},
),
datasets.SplitGenerator(
name=datasets.Split("dev"),
gen_kwargs={
"filepath": os.path.join(
data_dir, "dev", f"{task_name}_dev.csv"
),
},
),
]
def _generate_examples(self, filepath):
df = pd.read_csv(filepath,encoding="utf-8")
for i, instance in enumerate(df.to_dict(orient="records")):
if "answer" not in instance.keys():
instance["answer"]=""
if "explanation" not in instance.keys():
instance["explanation"]=""
yield i, instance |