Delete tmmluplus.py

tmmluplus.py

@@ -1,124 +0,0 @@
-# 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']
-                      }