GEO-bench / constants.py
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# metrics = ['relevance_detailed', 'uniqueness_detailed', 'subjcount_detailed', 'follow_detailed', 'simple_wordpos', 'simple_pos', 'influence_detailed', 'subjective_score', 'diversity_detailed', 'simple_word', 'subjpos_detailed']
columns = ['Method', 'Word', 'Position', 'WordPos Overall', 'Rel.', 'Infl.', 'Unique', 'Div.', 'FollowUp', 'Pos.', 'Count', 'Subjective Average', 'source']
metric_dict = {
'Word': 'simple_word',
'Position': 'simple_pos',
'WordPos Overall': 'simple_wordpos',
'Rel.': 'relevance_detailed',
'Infl.': 'influence_detailed',
'Unique': 'uniqueness_detailed',
'Div.': 'diversity_detailed',
'FollowUp': 'follow_detailed',
'Pos.': 'subjpos_detailed',
'Count': 'subjcount_detailed',
'Subjective Average': 'subjective_score',
}
tags = {
"Difficulty Level": ["Simple", "Intermediate", "Complex", "Multi-faceted", "Open-ended", 'any'],
"Nature of Query": ["Informational", "Navigational", "Transactional", "Debate", "Opinion", "Comparison", "Instructional", "Descriptive", "Predictive", 'any'],
"Sensitivity": ["Sensitive", "Non-sensitive",'any'],
"Genre": [
"๐ŸŽญ Arts and Entertainment", "๐Ÿš— Autos and Vehicles", "๐Ÿ’„ Beauty and Fitness", "๐Ÿ“š Books and Literature", "๐Ÿข Business and Industrial",
"๐Ÿ’ป Computers and Electronics", "๐Ÿ’ฐ Finance", "๐Ÿ” Food and Drink", "๐ŸŽฎ Games", "๐Ÿฅ Health", "๐ŸŽจ Hobbies and Leisure", "๐Ÿก Home and Garden",
"๐ŸŒ Internet and Telecom", "๐ŸŽ“ Jobs and Education", "๐Ÿ›๏ธ Law and Government", "๐Ÿ“ฐ News", "๐Ÿ’ฌ Online Communities", "๐Ÿ‘ซ People and Society",
"๐Ÿพ Pets and Animals", "๐Ÿก Real Estate", "๐Ÿ“š Reference", "๐Ÿ”ฌ Science", "๐Ÿ›’ Shopping", "โšฝ Sports", "โœˆ๏ธ Travel",'any'
],
"Specific Topics": ["Physics", "Chemistry", "Biology", "Mathematics", "Computer Science", "Economics", 'any'],
"User Intent": ["๐Ÿ” Research", "๐Ÿ’ฐ Purchase", "๐ŸŽ‰ Entertainment", "๐Ÿ“š Learning", "๐Ÿ”„ Comparison", 'any'],
"Answer Type": ["Fact", "Opinion", "List", "Explanation", "Guide", "Comparison", "Prediction", 'any'],
}