Datasets:
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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
_DESCRIPTION = """\
TMMLU2 data loader
"""
_DATA_PATH = "data"
task_list = [
'dentistry', 'traditional_chinese_medicine_clinical_medicine', 'clinical_psychology',
'technical', 'culinary_skills', 'mechanical', 'logic_reasoning', 'real_estate',
'general_principles_of_law', 'finance_banking', 'anti_money_laundering', 'ttqav2',
'marketing_management', 'business_management', 'organic_chemistry', 'advance_chemistry',
'physics', 'secondary_physics', 'human_behavior', 'national_protection', 'jce_humanities',
'politic_science', 'agriculture', 'official_document_management',
'financial_analysis', 'pharmacy', 'educational_psychology', 'statistics_and_machine_learning',
'management_accounting', 'introduction_to_law', 'computer_science', 'veterinary_pathology',
'accounting', 'fire_science', 'optometry', 'insurance_studies', 'pharmacology', 'taxation',
'education_(profession_level)', 'economics',
'veterinary_pharmacology', 'nautical_science', 'occupational_therapy_for_psychological_disorders',
'trust_practice', 'geography_of_taiwan', 'physical_education', 'auditing', 'administrative_law',
'basic_medical_science', 'macroeconomics', 'trade', 'chinese_language_and_literature',
'tve_design', 'junior_science_exam', 'junior_math_exam', 'junior_chinese_exam',
'junior_social_studies', 'tve_mathematics', 'tve_chinese_language',
'tve_natural_sciences', 'junior_chemistry', 'music', 'education',
'three_principles_of_people', 'taiwanese_hokkien',
'engineering_math'
]
_URLs = {
task_name: {
split_name: [
os.path.join(
_DATA_PATH, task_name+"_"+split_name+".csv"
), # TODO -> handle multiple shards
]
for split_name in ['dev', 'test', 'val']
}
for task_name in task_list
}
class TMMLU2Config(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(version=datasets.Version("1.0.0"), **kwargs)
class TMMLU2(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
TMMLU2Config(
name=task_name,
)
for task_name in task_list
]
def _info(self):
features = datasets.Features(
{
"question": datasets.Value("string"),
"A": datasets.Value("string"),
"B": datasets.Value("string"),
"C": datasets.Value("string"),
"D": datasets.Value("string"),
"answer": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
)
def _split_generators(self, dl_manager):
task_name = self.config.name
data_dir = dl_manager.download(_URLs[task_name])
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir['test'],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir['val'],
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir['dev'],
},
),
]
def _generate_examples(self, filepath):
if isinstance(filepath, list):
filepath = filepath[0]
df = pd.read_csv(filepath)
for i, instance in enumerate(df.to_dict(orient="records")):
yield i, {'question': instance['question'],
'A': instance['A'],
'B': instance['B'],
'C': instance['C'],
'D': instance['D'],
'answer': instance['answer']
} |