2656 lines
131 KiB
JSON
2656 lines
131 KiB
JSON
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{
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"results": {
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|
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"openaimmlu": {
|
||
|
|
"acc,none": 0.47728243839908846,
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||
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|
"acc_stderr,none": 0.004075228135853262,
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||
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"alias": "openaimmlu"
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||
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|
},
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||
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"openaimmlu_STEM": {
|
||
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"acc,none": 0.40066225165562913,
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||
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"acc_stderr,none": 0.008735985110676752,
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||
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"alias": " - STEM"
|
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},
|
||
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|
"openaimmlu_abstract_algebra": {
|
||
|
|
"alias": " - abstract_algebra",
|
||
|
|
"acc,none": 0.25,
|
||
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|
"acc_stderr,none": 0.04351941398892446
|
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|
},
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||
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"openaimmlu_astronomy": {
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||
|
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"alias": " - astronomy",
|
||
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"acc,none": 0.5197368421052632,
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||
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|
"acc_stderr,none": 0.040657710025626036
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},
|
||
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"openaimmlu_college_biology": {
|
||
|
|
"alias": " - college_biology",
|
||
|
|
"acc,none": 0.5277777777777778,
|
||
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|
"acc_stderr,none": 0.04174752578923185
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||
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},
|
||
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"openaimmlu_college_chemistry": {
|
||
|
|
"alias": " - college_chemistry",
|
||
|
|
"acc,none": 0.34,
|
||
|
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"acc_stderr,none": 0.04760952285695236
|
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},
|
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"openaimmlu_college_computer_science": {
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|
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"alias": " - college_computer_science",
|
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"acc,none": 0.46,
|
||
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|
"acc_stderr,none": 0.05009082659620333
|
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},
|
||
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|
"openaimmlu_college_mathematics": {
|
||
|
|
"alias": " - college_mathematics",
|
||
|
|
"acc,none": 0.29,
|
||
|
|
"acc_stderr,none": 0.04560480215720684
|
||
|
|
},
|
||
|
|
"openaimmlu_college_physics": {
|
||
|
|
"alias": " - college_physics",
|
||
|
|
"acc,none": 0.3333333333333333,
|
||
|
|
"acc_stderr,none": 0.04690650298201943
|
||
|
|
},
|
||
|
|
"openaimmlu_computer_security": {
|
||
|
|
"alias": " - computer_security",
|
||
|
|
"acc,none": 0.57,
|
||
|
|
"acc_stderr,none": 0.04975698519562428
|
||
|
|
},
|
||
|
|
"openaimmlu_conceptual_physics": {
|
||
|
|
"alias": " - conceptual_physics",
|
||
|
|
"acc,none": 0.3872340425531915,
|
||
|
|
"acc_stderr,none": 0.03184389265339526
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||
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|
},
|
||
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|
"openaimmlu_econometrics": {
|
||
|
|
"alias": " - econometrics",
|
||
|
|
"acc,none": 0.3333333333333333,
|
||
|
|
"acc_stderr,none": 0.044346007015849245
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||
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},
|
||
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"openaimmlu_electrical_engineering": {
|
||
|
|
"alias": " - electrical_engineering",
|
||
|
|
"acc,none": 0.4689655172413793,
|
||
|
|
"acc_stderr,none": 0.04158632762097828
|
||
|
|
},
|
||
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|
"openaimmlu_elementary_mathematics": {
|
||
|
|
"alias": " - elementary_mathematics",
|
||
|
|
"acc,none": 0.3412698412698413,
|
||
|
|
"acc_stderr,none": 0.02441923496681907
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_biology": {
|
||
|
|
"alias": " - high_school_biology",
|
||
|
|
"acc,none": 0.5838709677419355,
|
||
|
|
"acc_stderr,none": 0.028040981380761543
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||
|
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},
|
||
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|
"openaimmlu_high_school_chemistry": {
|
||
|
|
"alias": " - high_school_chemistry",
|
||
|
|
"acc,none": 0.4236453201970443,
|
||
|
|
"acc_stderr,none": 0.034767257476490364
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_computer_science": {
|
||
|
|
"alias": " - high_school_computer_science",
|
||
|
|
"acc,none": 0.49,
|
||
|
|
"acc_stderr,none": 0.05024183937956912
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_mathematics": {
|
||
|
|
"alias": " - high_school_mathematics",
|
||
|
|
"acc,none": 0.29259259259259257,
|
||
|
|
"acc_stderr,none": 0.02773896963217609
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_physics": {
|
||
|
|
"alias": " - high_school_physics",
|
||
|
|
"acc,none": 0.33112582781456956,
|
||
|
|
"acc_stderr,none": 0.038425817186598696
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_statistics": {
|
||
|
|
"alias": " - high_school_statistics",
|
||
|
|
"acc,none": 0.27314814814814814,
|
||
|
|
"acc_stderr,none": 0.030388051301678116
|
||
|
|
},
|
||
|
|
"openaimmlu_humanities": {
|
||
|
|
"acc,none": 0.6003325942350333,
|
||
|
|
"acc_stderr,none": 0.011449323544037743,
|
||
|
|
"alias": " - Humanities"
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_european_history": {
|
||
|
|
"alias": " - high_school_european_history",
|
||
|
|
"acc,none": 0.6909090909090909,
|
||
|
|
"acc_stderr,none": 0.036085410115739666
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_us_history": {
|
||
|
|
"alias": " - high_school_us_history",
|
||
|
|
"acc,none": 0.6323529411764706,
|
||
|
|
"acc_stderr,none": 0.03384132045674118
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_world_history": {
|
||
|
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"alias": " - high_school_world_history",
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|
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"acc,none": 0.6835443037974683,
|
||
|
|
"acc_stderr,none": 0.03027497488021898
|
||
|
|
},
|
||
|
|
"openaimmlu_international_law": {
|
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|
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"alias": " - international_law",
|
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|
|
"acc,none": 0.6446280991735537,
|
||
|
|
"acc_stderr,none": 0.0436923632657398
|
||
|
|
},
|
||
|
|
"openaimmlu_jurisprudence": {
|
||
|
|
"alias": " - jurisprudence",
|
||
|
|
"acc,none": 0.5555555555555556,
|
||
|
|
"acc_stderr,none": 0.04803752235190192
|
||
|
|
},
|
||
|
|
"openaimmlu_logical_fallacies": {
|
||
|
|
"alias": " - logical_fallacies",
|
||
|
|
"acc,none": 0.5521472392638037,
|
||
|
|
"acc_stderr,none": 0.03906947479456606
|
||
|
|
},
|
||
|
|
"openaimmlu_philosophy": {
|
||
|
|
"alias": " - philosophy",
|
||
|
|
"acc,none": 0.5530546623794212,
|
||
|
|
"acc_stderr,none": 0.028237769422085335
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||
|
|
},
|
||
|
|
"openaimmlu_prehistory": {
|
||
|
|
"alias": " - prehistory",
|
||
|
|
"acc,none": 0.5061728395061729,
|
||
|
|
"acc_stderr,none": 0.027818623962583302
|
||
|
|
},
|
||
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|
"openaimmlu_world_religions": {
|
||
|
|
"alias": " - world_religions",
|
||
|
|
"acc,none": 0.6666666666666666,
|
||
|
|
"acc_stderr,none": 0.036155076303109344
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||
|
|
},
|
||
|
|
"openaimmlu_other": {
|
||
|
|
"acc,none": 0.48128792987188135,
|
||
|
|
"acc_stderr,none": 0.006333441327132957,
|
||
|
|
"alias": " - Other"
|
||
|
|
},
|
||
|
|
"openaimmlu_anatomy": {
|
||
|
|
"alias": " - anatomy",
|
||
|
|
"acc,none": 0.4444444444444444,
|
||
|
|
"acc_stderr,none": 0.04292596718256981
|
||
|
|
},
|
||
|
|
"openaimmlu_clinical_knowledge": {
|
||
|
|
"alias": " - clinical_knowledge",
|
||
|
|
"acc,none": 0.5509433962264151,
|
||
|
|
"acc_stderr,none": 0.030612730713641095
|
||
|
|
},
|
||
|
|
"openaimmlu_college_medicine": {
|
||
|
|
"alias": " - college_medicine",
|
||
|
|
"acc,none": 0.49710982658959535,
|
||
|
|
"acc_stderr,none": 0.038124005659748335
|
||
|
|
},
|
||
|
|
"openaimmlu_formal_logic": {
|
||
|
|
"alias": " - formal_logic",
|
||
|
|
"acc,none": 0.35714285714285715,
|
||
|
|
"acc_stderr,none": 0.04285714285714281
|
||
|
|
},
|
||
|
|
"openaimmlu_global_facts": {
|
||
|
|
"alias": " - global_facts",
|
||
|
|
"acc,none": 0.32,
|
||
|
|
"acc_stderr,none": 0.04688261722621504
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_geography": {
|
||
|
|
"alias": " - high_school_geography",
|
||
|
|
"acc,none": 0.6515151515151515,
|
||
|
|
"acc_stderr,none": 0.033948539651564025
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_psychology": {
|
||
|
|
"alias": " - high_school_psychology",
|
||
|
|
"acc,none": 0.6220183486238532,
|
||
|
|
"acc_stderr,none": 0.020789187066728106
|
||
|
|
},
|
||
|
|
"openaimmlu_human_aging": {
|
||
|
|
"alias": " - human_aging",
|
||
|
|
"acc,none": 0.547085201793722,
|
||
|
|
"acc_stderr,none": 0.033408675019233246
|
||
|
|
},
|
||
|
|
"openaimmlu_machine_learning": {
|
||
|
|
"alias": " - machine_learning",
|
||
|
|
"acc,none": 0.375,
|
||
|
|
"acc_stderr,none": 0.04595091388086298
|
||
|
|
},
|
||
|
|
"openaimmlu_medical_genetics": {
|
||
|
|
"alias": " - medical_genetics",
|
||
|
|
"acc,none": 0.6,
|
||
|
|
"acc_stderr,none": 0.04923659639173309
|
||
|
|
},
|
||
|
|
"openaimmlu_miscellaneous": {
|
||
|
|
"alias": " - miscellaneous",
|
||
|
|
"acc,none": 0.6257982120051085,
|
||
|
|
"acc_stderr,none": 0.01730480507225203
|
||
|
|
},
|
||
|
|
"openaimmlu_nutrition": {
|
||
|
|
"alias": " - nutrition",
|
||
|
|
"acc,none": 0.5359477124183006,
|
||
|
|
"acc_stderr,none": 0.02855582751652878
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_accounting": {
|
||
|
|
"alias": " - professional_accounting",
|
||
|
|
"acc,none": 0.37943262411347517,
|
||
|
|
"acc_stderr,none": 0.028947338851614105
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_law": {
|
||
|
|
"alias": " - professional_law",
|
||
|
|
"acc,none": 0.3500651890482399,
|
||
|
|
"acc_stderr,none": 0.012182552313215175
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_medicine": {
|
||
|
|
"alias": " - professional_medicine",
|
||
|
|
"acc,none": 0.4338235294117647,
|
||
|
|
"acc_stderr,none": 0.030105636570016633
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_psychology": {
|
||
|
|
"alias": " - professional_psychology",
|
||
|
|
"acc,none": 0.4869281045751634,
|
||
|
|
"acc_stderr,none": 0.020220920829626912
|
||
|
|
},
|
||
|
|
"openaimmlu_virology": {
|
||
|
|
"alias": " - virology",
|
||
|
|
"acc,none": 0.4819277108433735,
|
||
|
|
"acc_stderr,none": 0.03889951252827216
|
||
|
|
},
|
||
|
|
"openaimmlu_social_science": {
|
||
|
|
"acc,none": 0.472915398660986,
|
||
|
|
"acc_stderr,none": 0.008280814440523745,
|
||
|
|
"alias": " - Social Science"
|
||
|
|
},
|
||
|
|
"openaimmlu_business_ethics": {
|
||
|
|
"alias": " - business_ethics",
|
||
|
|
"acc,none": 0.58,
|
||
|
|
"acc_stderr,none": 0.049604496374885836
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_government_and_politics": {
|
||
|
|
"alias": " - high_school_government_and_politics",
|
||
|
|
"acc,none": 0.694300518134715,
|
||
|
|
"acc_stderr,none": 0.033248379397581594
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_macroeconomics": {
|
||
|
|
"alias": " - high_school_macroeconomics",
|
||
|
|
"acc,none": 0.4846153846153846,
|
||
|
|
"acc_stderr,none": 0.025339003010106515
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_microeconomics": {
|
||
|
|
"alias": " - high_school_microeconomics",
|
||
|
|
"acc,none": 0.42436974789915966,
|
||
|
|
"acc_stderr,none": 0.032104790510157764
|
||
|
|
},
|
||
|
|
"openaimmlu_human_sexuality": {
|
||
|
|
"alias": " - human_sexuality",
|
||
|
|
"acc,none": 0.6106870229007634,
|
||
|
|
"acc_stderr,none": 0.04276486542814591
|
||
|
|
},
|
||
|
|
"openaimmlu_management": {
|
||
|
|
"alias": " - management",
|
||
|
|
"acc,none": 0.5825242718446602,
|
||
|
|
"acc_stderr,none": 0.048828405482122375
|
||
|
|
},
|
||
|
|
"openaimmlu_marketing": {
|
||
|
|
"alias": " - marketing",
|
||
|
|
"acc,none": 0.6196581196581197,
|
||
|
|
"acc_stderr,none": 0.03180425204384099
|
||
|
|
},
|
||
|
|
"openaimmlu_moral_disputes": {
|
||
|
|
"alias": " - moral_disputes",
|
||
|
|
"acc,none": 0.5433526011560693,
|
||
|
|
"acc_stderr,none": 0.026817718130348916
|
||
|
|
},
|
||
|
|
"openaimmlu_moral_scenarios": {
|
||
|
|
"alias": " - moral_scenarios",
|
||
|
|
"acc,none": 0.24022346368715083,
|
||
|
|
"acc_stderr,none": 0.014288343803925315
|
||
|
|
},
|
||
|
|
"openaimmlu_public_relations": {
|
||
|
|
"alias": " - public_relations",
|
||
|
|
"acc,none": 0.44545454545454544,
|
||
|
|
"acc_stderr,none": 0.047605488214603246
|
||
|
|
},
|
||
|
|
"openaimmlu_security_studies": {
|
||
|
|
"alias": " - security_studies",
|
||
|
|
"acc,none": 0.5836734693877551,
|
||
|
|
"acc_stderr,none": 0.03155782816556165
|
||
|
|
},
|
||
|
|
"openaimmlu_sociology": {
|
||
|
|
"alias": " - sociology",
|
||
|
|
"acc,none": 0.6218905472636815,
|
||
|
|
"acc_stderr,none": 0.034288678487786564
|
||
|
|
},
|
||
|
|
"openaimmlu_us_foreign_policy": {
|
||
|
|
"alias": " - us_foreign_policy",
|
||
|
|
"acc,none": 0.67,
|
||
|
|
"acc_stderr,none": 0.047258156262526094
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"groups": {
|
||
|
|
"openaimmlu": {
|
||
|
|
"acc,none": 0.47728243839908846,
|
||
|
|
"acc_stderr,none": 0.004075228135853262,
|
||
|
|
"alias": "openaimmlu"
|
||
|
|
},
|
||
|
|
"openaimmlu_STEM": {
|
||
|
|
"acc,none": 0.40066225165562913,
|
||
|
|
"acc_stderr,none": 0.008735985110676752,
|
||
|
|
"alias": " - STEM"
|
||
|
|
},
|
||
|
|
"openaimmlu_humanities": {
|
||
|
|
"acc,none": 0.6003325942350333,
|
||
|
|
"acc_stderr,none": 0.011449323544037743,
|
||
|
|
"alias": " - Humanities"
|
||
|
|
},
|
||
|
|
"openaimmlu_other": {
|
||
|
|
"acc,none": 0.48128792987188135,
|
||
|
|
"acc_stderr,none": 0.006333441327132957,
|
||
|
|
"alias": " - Other"
|
||
|
|
},
|
||
|
|
"openaimmlu_social_science": {
|
||
|
|
"acc,none": 0.472915398660986,
|
||
|
|
"acc_stderr,none": 0.008280814440523745,
|
||
|
|
"alias": " - Social Science"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"group_subtasks": {
|
||
|
|
"openaimmlu_humanities": [
|
||
|
|
"openaimmlu_logical_fallacies",
|
||
|
|
"openaimmlu_philosophy",
|
||
|
|
"openaimmlu_international_law",
|
||
|
|
"openaimmlu_high_school_us_history",
|
||
|
|
"openaimmlu_jurisprudence",
|
||
|
|
"openaimmlu_prehistory",
|
||
|
|
"openaimmlu_world_religions",
|
||
|
|
"openaimmlu_high_school_world_history",
|
||
|
|
"openaimmlu_high_school_european_history"
|
||
|
|
],
|
||
|
|
"openaimmlu_social_science": [
|
||
|
|
"openaimmlu_us_foreign_policy",
|
||
|
|
"openaimmlu_high_school_microeconomics",
|
||
|
|
"openaimmlu_moral_scenarios",
|
||
|
|
"openaimmlu_high_school_government_and_politics",
|
||
|
|
"openaimmlu_management",
|
||
|
|
"openaimmlu_moral_disputes",
|
||
|
|
"openaimmlu_public_relations",
|
||
|
|
"openaimmlu_human_sexuality",
|
||
|
|
"openaimmlu_security_studies",
|
||
|
|
"openaimmlu_business_ethics",
|
||
|
|
"openaimmlu_sociology",
|
||
|
|
"openaimmlu_high_school_macroeconomics",
|
||
|
|
"openaimmlu_marketing"
|
||
|
|
],
|
||
|
|
"openaimmlu_other": [
|
||
|
|
"openaimmlu_professional_law",
|
||
|
|
"openaimmlu_professional_psychology",
|
||
|
|
"openaimmlu_machine_learning",
|
||
|
|
"openaimmlu_human_aging",
|
||
|
|
"openaimmlu_high_school_geography",
|
||
|
|
"openaimmlu_anatomy",
|
||
|
|
"openaimmlu_college_medicine",
|
||
|
|
"openaimmlu_professional_medicine",
|
||
|
|
"openaimmlu_global_facts",
|
||
|
|
"openaimmlu_medical_genetics",
|
||
|
|
"openaimmlu_miscellaneous",
|
||
|
|
"openaimmlu_nutrition",
|
||
|
|
"openaimmlu_formal_logic",
|
||
|
|
"openaimmlu_high_school_psychology",
|
||
|
|
"openaimmlu_clinical_knowledge",
|
||
|
|
"openaimmlu_virology",
|
||
|
|
"openaimmlu_professional_accounting"
|
||
|
|
],
|
||
|
|
"openaimmlu_STEM": [
|
||
|
|
"openaimmlu_college_mathematics",
|
||
|
|
"openaimmlu_abstract_algebra",
|
||
|
|
"openaimmlu_college_biology",
|
||
|
|
"openaimmlu_computer_security",
|
||
|
|
"openaimmlu_high_school_biology",
|
||
|
|
"openaimmlu_college_physics",
|
||
|
|
"openaimmlu_high_school_physics",
|
||
|
|
"openaimmlu_elementary_mathematics",
|
||
|
|
"openaimmlu_conceptual_physics",
|
||
|
|
"openaimmlu_high_school_computer_science",
|
||
|
|
"openaimmlu_high_school_statistics",
|
||
|
|
"openaimmlu_electrical_engineering",
|
||
|
|
"openaimmlu_college_computer_science",
|
||
|
|
"openaimmlu_high_school_chemistry",
|
||
|
|
"openaimmlu_college_chemistry",
|
||
|
|
"openaimmlu_econometrics",
|
||
|
|
"openaimmlu_astronomy",
|
||
|
|
"openaimmlu_high_school_mathematics"
|
||
|
|
],
|
||
|
|
"openaimmlu": [
|
||
|
|
"openaimmlu_STEM",
|
||
|
|
"openaimmlu_other",
|
||
|
|
"openaimmlu_social_science",
|
||
|
|
"openaimmlu_humanities"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"configs": {
|
||
|
|
"openaimmlu_abstract_algebra": {
|
||
|
|
"task": "openaimmlu_abstract_algebra",
|
||
|
|
"task_alias": "abstract_algebra",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "abstract_algebra",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_anatomy": {
|
||
|
|
"task": "openaimmlu_anatomy",
|
||
|
|
"task_alias": "anatomy",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "anatomy",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_astronomy": {
|
||
|
|
"task": "openaimmlu_astronomy",
|
||
|
|
"task_alias": "astronomy",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "astronomy",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_business_ethics": {
|
||
|
|
"task": "openaimmlu_business_ethics",
|
||
|
|
"task_alias": "business_ethics",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "business_ethics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_clinical_knowledge": {
|
||
|
|
"task": "openaimmlu_clinical_knowledge",
|
||
|
|
"task_alias": "clinical_knowledge",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "clinical_knowledge",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_college_biology": {
|
||
|
|
"task": "openaimmlu_college_biology",
|
||
|
|
"task_alias": "college_biology",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "college_biology",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_college_chemistry": {
|
||
|
|
"task": "openaimmlu_college_chemistry",
|
||
|
|
"task_alias": "college_chemistry",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "college_chemistry",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_college_computer_science": {
|
||
|
|
"task": "openaimmlu_college_computer_science",
|
||
|
|
"task_alias": "college_computer_science",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "college_computer_science",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_college_mathematics": {
|
||
|
|
"task": "openaimmlu_college_mathematics",
|
||
|
|
"task_alias": "college_mathematics",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "college_mathematics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_college_medicine": {
|
||
|
|
"task": "openaimmlu_college_medicine",
|
||
|
|
"task_alias": "college_medicine",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "college_medicine",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_college_physics": {
|
||
|
|
"task": "openaimmlu_college_physics",
|
||
|
|
"task_alias": "college_physics",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "college_physics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_computer_security": {
|
||
|
|
"task": "openaimmlu_computer_security",
|
||
|
|
"task_alias": "computer_security",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "computer_security",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_conceptual_physics": {
|
||
|
|
"task": "openaimmlu_conceptual_physics",
|
||
|
|
"task_alias": "conceptual_physics",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "conceptual_physics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_econometrics": {
|
||
|
|
"task": "openaimmlu_econometrics",
|
||
|
|
"task_alias": "econometrics",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "econometrics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_electrical_engineering": {
|
||
|
|
"task": "openaimmlu_electrical_engineering",
|
||
|
|
"task_alias": "electrical_engineering",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "electrical_engineering",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_elementary_mathematics": {
|
||
|
|
"task": "openaimmlu_elementary_mathematics",
|
||
|
|
"task_alias": "elementary_mathematics",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "elementary_mathematics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_formal_logic": {
|
||
|
|
"task": "openaimmlu_formal_logic",
|
||
|
|
"task_alias": "formal_logic",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "formal_logic",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_global_facts": {
|
||
|
|
"task": "openaimmlu_global_facts",
|
||
|
|
"task_alias": "global_facts",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "global_facts",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_biology": {
|
||
|
|
"task": "openaimmlu_high_school_biology",
|
||
|
|
"task_alias": "high_school_biology",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_biology",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_chemistry": {
|
||
|
|
"task": "openaimmlu_high_school_chemistry",
|
||
|
|
"task_alias": "high_school_chemistry",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_chemistry",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_computer_science": {
|
||
|
|
"task": "openaimmlu_high_school_computer_science",
|
||
|
|
"task_alias": "high_school_computer_science",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_computer_science",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_european_history": {
|
||
|
|
"task": "openaimmlu_high_school_european_history",
|
||
|
|
"task_alias": "high_school_european_history",
|
||
|
|
"tag": "openaimmlu_humanities_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_european_history",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_geography": {
|
||
|
|
"task": "openaimmlu_high_school_geography",
|
||
|
|
"task_alias": "high_school_geography",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_geography",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_government_and_politics": {
|
||
|
|
"task": "openaimmlu_high_school_government_and_politics",
|
||
|
|
"task_alias": "high_school_government_and_politics",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_government_and_politics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_macroeconomics": {
|
||
|
|
"task": "openaimmlu_high_school_macroeconomics",
|
||
|
|
"task_alias": "high_school_macroeconomics",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_macroeconomics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_mathematics": {
|
||
|
|
"task": "openaimmlu_high_school_mathematics",
|
||
|
|
"task_alias": "high_school_mathematics",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_mathematics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_microeconomics": {
|
||
|
|
"task": "openaimmlu_high_school_microeconomics",
|
||
|
|
"task_alias": "high_school_microeconomics",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_microeconomics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_physics": {
|
||
|
|
"task": "openaimmlu_high_school_physics",
|
||
|
|
"task_alias": "high_school_physics",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_physics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_psychology": {
|
||
|
|
"task": "openaimmlu_high_school_psychology",
|
||
|
|
"task_alias": "high_school_psychology",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_psychology",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_statistics": {
|
||
|
|
"task": "openaimmlu_high_school_statistics",
|
||
|
|
"task_alias": "high_school_statistics",
|
||
|
|
"tag": "openaimmlu_STEM_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_statistics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_us_history": {
|
||
|
|
"task": "openaimmlu_high_school_us_history",
|
||
|
|
"task_alias": "high_school_us_history",
|
||
|
|
"tag": "openaimmlu_humanities_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_us_history",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_world_history": {
|
||
|
|
"task": "openaimmlu_high_school_world_history",
|
||
|
|
"task_alias": "high_school_world_history",
|
||
|
|
"tag": "openaimmlu_humanities_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "high_school_world_history",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_human_aging": {
|
||
|
|
"task": "openaimmlu_human_aging",
|
||
|
|
"task_alias": "human_aging",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "human_aging",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_human_sexuality": {
|
||
|
|
"task": "openaimmlu_human_sexuality",
|
||
|
|
"task_alias": "human_sexuality",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "human_sexuality",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_international_law": {
|
||
|
|
"task": "openaimmlu_international_law",
|
||
|
|
"task_alias": "international_law",
|
||
|
|
"tag": "openaimmlu_humanities_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "international_law",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_jurisprudence": {
|
||
|
|
"task": "openaimmlu_jurisprudence",
|
||
|
|
"task_alias": "jurisprudence",
|
||
|
|
"tag": "openaimmlu_humanities_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "jurisprudence",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_logical_fallacies": {
|
||
|
|
"task": "openaimmlu_logical_fallacies",
|
||
|
|
"task_alias": "logical_fallacies",
|
||
|
|
"tag": "openaimmlu_humanities_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "logical_fallacies",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_machine_learning": {
|
||
|
|
"task": "openaimmlu_machine_learning",
|
||
|
|
"task_alias": "machine_learning",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "machine_learning",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_management": {
|
||
|
|
"task": "openaimmlu_management",
|
||
|
|
"task_alias": "management",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "management",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_marketing": {
|
||
|
|
"task": "openaimmlu_marketing",
|
||
|
|
"task_alias": "marketing",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "marketing",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_medical_genetics": {
|
||
|
|
"task": "openaimmlu_medical_genetics",
|
||
|
|
"task_alias": "medical_genetics",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "medical_genetics",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_miscellaneous": {
|
||
|
|
"task": "openaimmlu_miscellaneous",
|
||
|
|
"task_alias": "miscellaneous",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "miscellaneous",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_moral_disputes": {
|
||
|
|
"task": "openaimmlu_moral_disputes",
|
||
|
|
"task_alias": "moral_disputes",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "moral_disputes",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_moral_scenarios": {
|
||
|
|
"task": "openaimmlu_moral_scenarios",
|
||
|
|
"task_alias": "moral_scenarios",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "moral_scenarios",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_nutrition": {
|
||
|
|
"task": "openaimmlu_nutrition",
|
||
|
|
"task_alias": "nutrition",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "nutrition",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_philosophy": {
|
||
|
|
"task": "openaimmlu_philosophy",
|
||
|
|
"task_alias": "philosophy",
|
||
|
|
"tag": "openaimmlu_humanities_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "philosophy",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_prehistory": {
|
||
|
|
"task": "openaimmlu_prehistory",
|
||
|
|
"task_alias": "prehistory",
|
||
|
|
"tag": "openaimmlu_humanities_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "prehistory",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_accounting": {
|
||
|
|
"task": "openaimmlu_professional_accounting",
|
||
|
|
"task_alias": "professional_accounting",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "professional_accounting",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_law": {
|
||
|
|
"task": "openaimmlu_professional_law",
|
||
|
|
"task_alias": "professional_law",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "professional_law",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_medicine": {
|
||
|
|
"task": "openaimmlu_professional_medicine",
|
||
|
|
"task_alias": "professional_medicine",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "professional_medicine",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_psychology": {
|
||
|
|
"task": "openaimmlu_professional_psychology",
|
||
|
|
"task_alias": "professional_psychology",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "professional_psychology",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_public_relations": {
|
||
|
|
"task": "openaimmlu_public_relations",
|
||
|
|
"task_alias": "public_relations",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "public_relations",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_security_studies": {
|
||
|
|
"task": "openaimmlu_security_studies",
|
||
|
|
"task_alias": "security_studies",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "security_studies",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_sociology": {
|
||
|
|
"task": "openaimmlu_sociology",
|
||
|
|
"task_alias": "sociology",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "sociology",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_us_foreign_policy": {
|
||
|
|
"task": "openaimmlu_us_foreign_policy",
|
||
|
|
"task_alias": "us_foreign_policy",
|
||
|
|
"tag": "openaimmlu_social_science_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "us_foreign_policy",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_virology": {
|
||
|
|
"task": "openaimmlu_virology",
|
||
|
|
"task_alias": "virology",
|
||
|
|
"tag": "openaimmlu_other_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "virology",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"openaimmlu_world_religions": {
|
||
|
|
"task": "openaimmlu_world_religions",
|
||
|
|
"task_alias": "world_religions",
|
||
|
|
"tag": "openaimmlu_humanities_tasks",
|
||
|
|
"dataset_path": "khalidalt/openai_mmlu_arabic",
|
||
|
|
"dataset_name": "world_religions",
|
||
|
|
"test_split": "test",
|
||
|
|
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
|
||
|
|
"doc_to_text": "query",
|
||
|
|
"doc_to_target": "gold",
|
||
|
|
"doc_to_choice": "choices",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"versions": {
|
||
|
|
"openaimmlu": 0,
|
||
|
|
"openaimmlu_STEM": 0,
|
||
|
|
"openaimmlu_abstract_algebra": 0.0,
|
||
|
|
"openaimmlu_anatomy": 0.0,
|
||
|
|
"openaimmlu_astronomy": 0.0,
|
||
|
|
"openaimmlu_business_ethics": 0.0,
|
||
|
|
"openaimmlu_clinical_knowledge": 0.0,
|
||
|
|
"openaimmlu_college_biology": 0.0,
|
||
|
|
"openaimmlu_college_chemistry": 0.0,
|
||
|
|
"openaimmlu_college_computer_science": 0.0,
|
||
|
|
"openaimmlu_college_mathematics": 0.0,
|
||
|
|
"openaimmlu_college_medicine": 0.0,
|
||
|
|
"openaimmlu_college_physics": 0.0,
|
||
|
|
"openaimmlu_computer_security": 0.0,
|
||
|
|
"openaimmlu_conceptual_physics": 0.0,
|
||
|
|
"openaimmlu_econometrics": 0.0,
|
||
|
|
"openaimmlu_electrical_engineering": 0.0,
|
||
|
|
"openaimmlu_elementary_mathematics": 0.0,
|
||
|
|
"openaimmlu_formal_logic": 0.0,
|
||
|
|
"openaimmlu_global_facts": 0.0,
|
||
|
|
"openaimmlu_high_school_biology": 0.0,
|
||
|
|
"openaimmlu_high_school_chemistry": 0.0,
|
||
|
|
"openaimmlu_high_school_computer_science": 0.0,
|
||
|
|
"openaimmlu_high_school_european_history": 0.0,
|
||
|
|
"openaimmlu_high_school_geography": 0.0,
|
||
|
|
"openaimmlu_high_school_government_and_politics": 0.0,
|
||
|
|
"openaimmlu_high_school_macroeconomics": 0.0,
|
||
|
|
"openaimmlu_high_school_mathematics": 0.0,
|
||
|
|
"openaimmlu_high_school_microeconomics": 0.0,
|
||
|
|
"openaimmlu_high_school_physics": 0.0,
|
||
|
|
"openaimmlu_high_school_psychology": 0.0,
|
||
|
|
"openaimmlu_high_school_statistics": 0.0,
|
||
|
|
"openaimmlu_high_school_us_history": 0.0,
|
||
|
|
"openaimmlu_high_school_world_history": 0.0,
|
||
|
|
"openaimmlu_human_aging": 0.0,
|
||
|
|
"openaimmlu_human_sexuality": 0.0,
|
||
|
|
"openaimmlu_humanities": 0,
|
||
|
|
"openaimmlu_international_law": 0.0,
|
||
|
|
"openaimmlu_jurisprudence": 0.0,
|
||
|
|
"openaimmlu_logical_fallacies": 0.0,
|
||
|
|
"openaimmlu_machine_learning": 0.0,
|
||
|
|
"openaimmlu_management": 0.0,
|
||
|
|
"openaimmlu_marketing": 0.0,
|
||
|
|
"openaimmlu_medical_genetics": 0.0,
|
||
|
|
"openaimmlu_miscellaneous": 0.0,
|
||
|
|
"openaimmlu_moral_disputes": 0.0,
|
||
|
|
"openaimmlu_moral_scenarios": 0.0,
|
||
|
|
"openaimmlu_nutrition": 0.0,
|
||
|
|
"openaimmlu_other": 0,
|
||
|
|
"openaimmlu_philosophy": 0.0,
|
||
|
|
"openaimmlu_prehistory": 0.0,
|
||
|
|
"openaimmlu_professional_accounting": 0.0,
|
||
|
|
"openaimmlu_professional_law": 0.0,
|
||
|
|
"openaimmlu_professional_medicine": 0.0,
|
||
|
|
"openaimmlu_professional_psychology": 0.0,
|
||
|
|
"openaimmlu_public_relations": 0.0,
|
||
|
|
"openaimmlu_security_studies": 0.0,
|
||
|
|
"openaimmlu_social_science": 0,
|
||
|
|
"openaimmlu_sociology": 0.0,
|
||
|
|
"openaimmlu_us_foreign_policy": 0.0,
|
||
|
|
"openaimmlu_virology": 0.0,
|
||
|
|
"openaimmlu_world_religions": 0.0
|
||
|
|
},
|
||
|
|
"n-shot": {
|
||
|
|
"openaimmlu_abstract_algebra": 0,
|
||
|
|
"openaimmlu_anatomy": 0,
|
||
|
|
"openaimmlu_astronomy": 0,
|
||
|
|
"openaimmlu_business_ethics": 0,
|
||
|
|
"openaimmlu_clinical_knowledge": 0,
|
||
|
|
"openaimmlu_college_biology": 0,
|
||
|
|
"openaimmlu_college_chemistry": 0,
|
||
|
|
"openaimmlu_college_computer_science": 0,
|
||
|
|
"openaimmlu_college_mathematics": 0,
|
||
|
|
"openaimmlu_college_medicine": 0,
|
||
|
|
"openaimmlu_college_physics": 0,
|
||
|
|
"openaimmlu_computer_security": 0,
|
||
|
|
"openaimmlu_conceptual_physics": 0,
|
||
|
|
"openaimmlu_econometrics": 0,
|
||
|
|
"openaimmlu_electrical_engineering": 0,
|
||
|
|
"openaimmlu_elementary_mathematics": 0,
|
||
|
|
"openaimmlu_formal_logic": 0,
|
||
|
|
"openaimmlu_global_facts": 0,
|
||
|
|
"openaimmlu_high_school_biology": 0,
|
||
|
|
"openaimmlu_high_school_chemistry": 0,
|
||
|
|
"openaimmlu_high_school_computer_science": 0,
|
||
|
|
"openaimmlu_high_school_european_history": 0,
|
||
|
|
"openaimmlu_high_school_geography": 0,
|
||
|
|
"openaimmlu_high_school_government_and_politics": 0,
|
||
|
|
"openaimmlu_high_school_macroeconomics": 0,
|
||
|
|
"openaimmlu_high_school_mathematics": 0,
|
||
|
|
"openaimmlu_high_school_microeconomics": 0,
|
||
|
|
"openaimmlu_high_school_physics": 0,
|
||
|
|
"openaimmlu_high_school_psychology": 0,
|
||
|
|
"openaimmlu_high_school_statistics": 0,
|
||
|
|
"openaimmlu_high_school_us_history": 0,
|
||
|
|
"openaimmlu_high_school_world_history": 0,
|
||
|
|
"openaimmlu_human_aging": 0,
|
||
|
|
"openaimmlu_human_sexuality": 0,
|
||
|
|
"openaimmlu_international_law": 0,
|
||
|
|
"openaimmlu_jurisprudence": 0,
|
||
|
|
"openaimmlu_logical_fallacies": 0,
|
||
|
|
"openaimmlu_machine_learning": 0,
|
||
|
|
"openaimmlu_management": 0,
|
||
|
|
"openaimmlu_marketing": 0,
|
||
|
|
"openaimmlu_medical_genetics": 0,
|
||
|
|
"openaimmlu_miscellaneous": 0,
|
||
|
|
"openaimmlu_moral_disputes": 0,
|
||
|
|
"openaimmlu_moral_scenarios": 0,
|
||
|
|
"openaimmlu_nutrition": 0,
|
||
|
|
"openaimmlu_philosophy": 0,
|
||
|
|
"openaimmlu_prehistory": 0,
|
||
|
|
"openaimmlu_professional_accounting": 0,
|
||
|
|
"openaimmlu_professional_law": 0,
|
||
|
|
"openaimmlu_professional_medicine": 0,
|
||
|
|
"openaimmlu_professional_psychology": 0,
|
||
|
|
"openaimmlu_public_relations": 0,
|
||
|
|
"openaimmlu_security_studies": 0,
|
||
|
|
"openaimmlu_sociology": 0,
|
||
|
|
"openaimmlu_us_foreign_policy": 0,
|
||
|
|
"openaimmlu_virology": 0,
|
||
|
|
"openaimmlu_world_religions": 0
|
||
|
|
},
|
||
|
|
"higher_is_better": {
|
||
|
|
"openaimmlu": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_STEM": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_abstract_algebra": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_anatomy": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_astronomy": {
|
||
|
|
"acc": true
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
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|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
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|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
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|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
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|
||
|
|
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|
||
|
|
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|
||
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|
||
|
|
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|
||
|
|
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|
||
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|
||
|
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|
||
|
|
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|
||
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|
||
|
|
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|
||
|
|
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|
||
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|
||
|
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|
||
|
|
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|
||
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||
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|
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|
||
|
|
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||
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|
||
|
|
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|
||
|
|
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|
||
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|
||
|
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|
||
|
|
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||
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||
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||
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||
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||
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|
||
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|
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||
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||
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||
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|
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||
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||
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||
|
|
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||
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||
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|
||
|
|
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||
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||
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|
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|
||
|
|
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||
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|
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||
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|
||
|
|
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||
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||
|
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|
||
|
|
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||
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|
||
|
|
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|
||
|
|
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|
||
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|
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|
||
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|
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|
||
|
|
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|
||
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|
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|
||
|
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|
||
|
|
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|
||
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
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|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
"acc": true
|
||
|
|
}
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
"effective": 193
|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
"effective": 311
|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
"original": 204,
|
||
|
|
"effective": 204
|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
"original": 324,
|
||
|
|
"effective": 324
|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_world_history": {
|
||
|
|
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|
||
|
|
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|
||
|
|
},
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
}
|
||
|
|
},
|
||
|
|
"config": {
|
||
|
|
"model": "vllm",
|
||
|
|
"model_args": "pretrained=inceptionai/jais-family-13b-chat,tensor_parallel_size=1,data_parallel_size=2,gpu_memory_utilization=0.4,download_dir=/tmp",
|
||
|
|
"batch_size": 1,
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
"bootstrap_iters": 100000,
|
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"date": 1735754494.9131842,
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"model_source": "vllm",
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"model_name": "inceptionai/jais-family-13b-chat",
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"model_name_sanitized": "inceptionai__jais-family-13b-chat",
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}
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