Upload tmmluplus.py

tmmluplus.py

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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']
+                      }