mirror of
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51 lines
3.0 KiB
Python
51 lines
3.0 KiB
Python
sub_categories = {
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'math': ['abstract_algebra', 'college_mathematics', 'elementary_mathematics', 'high_school_mathematics', 'high_school_statistics'],
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'health': ['anatomy', 'clinical_knowledge', 'college_medicine', 'human_aging', 'medical_genetics', 'nutrition', 'professional_medicine', 'virology'],
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'physics': ['astronomy', 'college_physics', 'conceptual_physics', 'high_school_physics'],
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'business': ['business_ethics', 'management', 'marketing'],
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'biology': ['college_biology', 'high_school_biology'],
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'chemistry': ['college_chemistry', 'high_school_chemistry'],
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'computer science': ['college_computer_science', 'computer_security', 'high_school_computer_science', 'machine_learning'],
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'economics': ['econometrics', 'high_school_macroeconomics', 'high_school_microeconomics'],
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'engineering': ['electrical_engineering'],
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'philosophy': ['formal_logic', 'logical_fallacies', 'moral_disputes', 'moral_scenarios', 'philosophy', 'world_religions'],
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'other': ['global_facts', 'miscellaneous', 'professional_accounting'],
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'history': ['high_school_european_history', 'high_school_us_history', 'high_school_world_history', 'prehistory'],
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'geography': ['high_school_geography'],
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'politics': ['high_school_government_and_politics', 'public_relations', 'security_studies', 'us_foreign_policy'],
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'psychology': ['high_school_psychology', 'professional_psychology'],
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'culture': ['human_sexuality', 'sociology'],
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'law': ['international_law', 'jurisprudence', 'professional_law']
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}
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categories = {
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'STEM': ['physics', 'chemistry', 'biology', 'computer science', 'math', 'engineering'],
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'humanities': ['history', 'philosophy', 'law'],
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'social_sciences': ['politics', 'culture', 'economics', 'geography', 'psychology'],
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'other': ['other', 'business', 'health'],
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}
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category2subject = {}
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for k, v in categories.items():
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for subject, subcat in sub_categories.items():
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if subject in v:
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for c in subcat:
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category2subject.setdefault(k, []).append(c)
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MMLUArabic_summary_groups = []
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_MMLUArabic_stem = ['acegpt_MMLUArabic_' + s for s in category2subject['STEM']]
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MMLUArabic_summary_groups.append({'name': 'acegpt_MMLUArabic_STEM', 'subsets': _MMLUArabic_stem})
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_MMLUArabic_humanities = ['acegpt_MMLUArabic_' + s for s in category2subject['humanities']]
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MMLUArabic_summary_groups.append({'name': 'acegpt_MMLUArabic_humanities', 'subsets': _MMLUArabic_humanities})
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_MMLUArabic_social_science = ['acegpt_MMLUArabic_' + s for s in category2subject['social_sciences']]
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MMLUArabic_summary_groups.append({'name': 'acegpt_MMLUArabic_social_science', 'subsets': _MMLUArabic_social_science})
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_MMLUArabic_other = ['acegpt_MMLUArabic_' + s for s in category2subject['other']]
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MMLUArabic_summary_groups.append({'name': 'acegpt_MMLUArabic_other', 'subsets': _MMLUArabic_other})
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_MMLUArabic_all = _MMLUArabic_stem + _MMLUArabic_humanities + _MMLUArabic_social_science + _MMLUArabic_other
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MMLUArabic_summary_groups.append({'name': 'acegpt_MMLUArabic', 'subsets': _MMLUArabic_all})
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