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Datasets:
ikala
/
tmmluplus

Tasks:
Question Answering
Modalities:
Text
Formats:
csv
Languages:
Chinese
Size:
10K - 100K
Tags:
traditional chinese
finance
medical
taiwan
benchmark
zh-tw
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
7
tmmluplus
File size: 4,784 Bytes 
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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']
                      }