2662 lines
131 KiB
JSON
2662 lines
131 KiB
JSON
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{
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"results": {
|
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|
|
"openaimmlu": {
|
||
|
|
"acc,none": 0.4615439396097422,
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"acc_stderr,none": 0.004090287961453241,
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||
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"alias": "openaimmlu"
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},
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||
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"openaimmlu_STEM": {
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"acc,none": 0.4198675496688742,
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"acc_stderr,none": 0.008819083118680756,
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"alias": " - STEM"
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},
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"openaimmlu_abstract_algebra": {
|
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|
"alias": " - abstract_algebra",
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"acc,none": 0.24,
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"acc_stderr,none": 0.042923469599092816
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},
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"openaimmlu_astronomy": {
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"alias": " - astronomy",
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"acc,none": 0.5197368421052632,
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"acc_stderr,none": 0.04065771002562603
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},
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"openaimmlu_college_biology": {
|
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"alias": " - college_biology",
|
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"acc,none": 0.4652777777777778,
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"acc_stderr,none": 0.041711158581816184
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},
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"openaimmlu_college_chemistry": {
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"alias": " - college_chemistry",
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"acc,none": 0.37,
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||
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"acc_stderr,none": 0.04852365870939099
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},
|
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"openaimmlu_college_computer_science": {
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"alias": " - college_computer_science",
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"acc,none": 0.36,
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"acc_stderr,none": 0.048241815132442176
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},
|
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|
"openaimmlu_college_mathematics": {
|
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|
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"alias": " - college_mathematics",
|
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"acc,none": 0.27,
|
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"acc_stderr,none": 0.044619604333847394
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|
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},
|
||
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"openaimmlu_college_physics": {
|
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|
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"alias": " - college_physics",
|
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"acc,none": 0.28431372549019607,
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"acc_stderr,none": 0.04488482852329017
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},
|
||
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"openaimmlu_computer_security": {
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||
|
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"alias": " - computer_security",
|
||
|
|
"acc,none": 0.52,
|
||
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"acc_stderr,none": 0.050211673156867795
|
||
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|
},
|
||
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"openaimmlu_conceptual_physics": {
|
||
|
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"alias": " - conceptual_physics",
|
||
|
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"acc,none": 0.4297872340425532,
|
||
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"acc_stderr,none": 0.03236214467715564
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||
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},
|
||
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"openaimmlu_econometrics": {
|
||
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"alias": " - econometrics",
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||
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"acc,none": 0.3333333333333333,
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||
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"acc_stderr,none": 0.044346007015849245
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},
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"openaimmlu_electrical_engineering": {
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"alias": " - electrical_engineering",
|
||
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"acc,none": 0.5241379310344828,
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"acc_stderr,none": 0.0416180850350153
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||
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},
|
||
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"openaimmlu_elementary_mathematics": {
|
||
|
|
"alias": " - elementary_mathematics",
|
||
|
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"acc,none": 0.3835978835978836,
|
||
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|
"acc_stderr,none": 0.025043757318520196
|
||
|
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},
|
||
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"openaimmlu_high_school_biology": {
|
||
|
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"alias": " - high_school_biology",
|
||
|
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"acc,none": 0.5935483870967742,
|
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"acc_stderr,none": 0.027941727346256308
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},
|
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"openaimmlu_high_school_chemistry": {
|
||
|
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"alias": " - high_school_chemistry",
|
||
|
|
"acc,none": 0.43349753694581283,
|
||
|
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"acc_stderr,none": 0.03486731727419872
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},
|
||
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"openaimmlu_high_school_computer_science": {
|
||
|
|
"alias": " - high_school_computer_science",
|
||
|
|
"acc,none": 0.57,
|
||
|
|
"acc_stderr,none": 0.04975698519562428
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_mathematics": {
|
||
|
|
"alias": " - high_school_mathematics",
|
||
|
|
"acc,none": 0.2962962962962963,
|
||
|
|
"acc_stderr,none": 0.02784081149587193
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||
|
|
},
|
||
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|
"openaimmlu_high_school_physics": {
|
||
|
|
"alias": " - high_school_physics",
|
||
|
|
"acc,none": 0.3443708609271523,
|
||
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|
"acc_stderr,none": 0.038796870240733264
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||
|
|
},
|
||
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|
"openaimmlu_high_school_statistics": {
|
||
|
|
"alias": " - high_school_statistics",
|
||
|
|
"acc,none": 0.4444444444444444,
|
||
|
|
"acc_stderr,none": 0.03388857118502325
|
||
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|
},
|
||
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|
"openaimmlu_humanities": {
|
||
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|
"acc,none": 0.5720620842572062,
|
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"acc_stderr,none": 0.011582619725483814,
|
||
|
|
"alias": " - Humanities"
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_european_history": {
|
||
|
|
"alias": " - high_school_european_history",
|
||
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|
"acc,none": 0.6606060606060606,
|
||
|
|
"acc_stderr,none": 0.03697442205031595
|
||
|
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},
|
||
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"openaimmlu_high_school_us_history": {
|
||
|
|
"alias": " - high_school_us_history",
|
||
|
|
"acc,none": 0.6176470588235294,
|
||
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|
"acc_stderr,none": 0.03410785338904719
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||
|
|
},
|
||
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|
"openaimmlu_high_school_world_history": {
|
||
|
|
"alias": " - high_school_world_history",
|
||
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|
"acc,none": 0.6624472573839663,
|
||
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|
"acc_stderr,none": 0.03078154910202622
|
||
|
|
},
|
||
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|
"openaimmlu_international_law": {
|
||
|
|
"alias": " - international_law",
|
||
|
|
"acc,none": 0.628099173553719,
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||
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"acc_stderr,none": 0.04412015806624505
|
||
|
|
},
|
||
|
|
"openaimmlu_jurisprudence": {
|
||
|
|
"alias": " - jurisprudence",
|
||
|
|
"acc,none": 0.5648148148148148,
|
||
|
|
"acc_stderr,none": 0.04792898170907062
|
||
|
|
},
|
||
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|
"openaimmlu_logical_fallacies": {
|
||
|
|
"alias": " - logical_fallacies",
|
||
|
|
"acc,none": 0.4723926380368098,
|
||
|
|
"acc_stderr,none": 0.03922378290610991
|
||
|
|
},
|
||
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|
"openaimmlu_philosophy": {
|
||
|
|
"alias": " - philosophy",
|
||
|
|
"acc,none": 0.5241157556270096,
|
||
|
|
"acc_stderr,none": 0.028365041542564577
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||
|
|
},
|
||
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|
"openaimmlu_prehistory": {
|
||
|
|
"alias": " - prehistory",
|
||
|
|
"acc,none": 0.5277777777777778,
|
||
|
|
"acc_stderr,none": 0.027777777777777797
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||
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},
|
||
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|
"openaimmlu_world_religions": {
|
||
|
|
"alias": " - world_religions",
|
||
|
|
"acc,none": 0.5380116959064327,
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||
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|
"acc_stderr,none": 0.03823727092882307
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||
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|
},
|
||
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"openaimmlu_other": {
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||
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||
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"acc_stderr,none": 0.0063302986349148774,
|
||
|
|
"alias": " - Other"
|
||
|
|
},
|
||
|
|
"openaimmlu_anatomy": {
|
||
|
|
"alias": " - anatomy",
|
||
|
|
"acc,none": 0.4444444444444444,
|
||
|
|
"acc_stderr,none": 0.04292596718256981
|
||
|
|
},
|
||
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|
"openaimmlu_clinical_knowledge": {
|
||
|
|
"alias": " - clinical_knowledge",
|
||
|
|
"acc,none": 0.5094339622641509,
|
||
|
|
"acc_stderr,none": 0.0307673947078081
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||
|
|
},
|
||
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|
"openaimmlu_college_medicine": {
|
||
|
|
"alias": " - college_medicine",
|
||
|
|
"acc,none": 0.41040462427745666,
|
||
|
|
"acc_stderr,none": 0.03750757044895537
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||
|
|
},
|
||
|
|
"openaimmlu_formal_logic": {
|
||
|
|
"alias": " - formal_logic",
|
||
|
|
"acc,none": 0.2619047619047619,
|
||
|
|
"acc_stderr,none": 0.03932537680392871
|
||
|
|
},
|
||
|
|
"openaimmlu_global_facts": {
|
||
|
|
"alias": " - global_facts",
|
||
|
|
"acc,none": 0.36,
|
||
|
|
"acc_stderr,none": 0.048241815132442176
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_geography": {
|
||
|
|
"alias": " - high_school_geography",
|
||
|
|
"acc,none": 0.5858585858585859,
|
||
|
|
"acc_stderr,none": 0.035094383488796295
|
||
|
|
},
|
||
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|
"openaimmlu_high_school_psychology": {
|
||
|
|
"alias": " - high_school_psychology",
|
||
|
|
"acc,none": 0.5614678899082569,
|
||
|
|
"acc_stderr,none": 0.021274713073954565
|
||
|
|
},
|
||
|
|
"openaimmlu_human_aging": {
|
||
|
|
"alias": " - human_aging",
|
||
|
|
"acc,none": 0.47085201793721976,
|
||
|
|
"acc_stderr,none": 0.03350073248773404
|
||
|
|
},
|
||
|
|
"openaimmlu_machine_learning": {
|
||
|
|
"alias": " - machine_learning",
|
||
|
|
"acc,none": 0.24107142857142858,
|
||
|
|
"acc_stderr,none": 0.04059867246952685
|
||
|
|
},
|
||
|
|
"openaimmlu_medical_genetics": {
|
||
|
|
"alias": " - medical_genetics",
|
||
|
|
"acc,none": 0.48,
|
||
|
|
"acc_stderr,none": 0.050211673156867795
|
||
|
|
},
|
||
|
|
"openaimmlu_miscellaneous": {
|
||
|
|
"alias": " - miscellaneous",
|
||
|
|
"acc,none": 0.5925925925925926,
|
||
|
|
"acc_stderr,none": 0.017570705239256555
|
||
|
|
},
|
||
|
|
"openaimmlu_nutrition": {
|
||
|
|
"alias": " - nutrition",
|
||
|
|
"acc,none": 0.5294117647058824,
|
||
|
|
"acc_stderr,none": 0.02858034106513829
|
||
|
|
},
|
||
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|
"openaimmlu_professional_accounting": {
|
||
|
|
"alias": " - professional_accounting",
|
||
|
|
"acc,none": 0.30851063829787234,
|
||
|
|
"acc_stderr,none": 0.027553366165101362
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_law": {
|
||
|
|
"alias": " - professional_law",
|
||
|
|
"acc,none": 0.3546284224250326,
|
||
|
|
"acc_stderr,none": 0.012218576439090169
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_medicine": {
|
||
|
|
"alias": " - professional_medicine",
|
||
|
|
"acc,none": 0.44485294117647056,
|
||
|
|
"acc_stderr,none": 0.03018753206032938
|
||
|
|
},
|
||
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|
"openaimmlu_professional_psychology": {
|
||
|
|
"alias": " - professional_psychology",
|
||
|
|
"acc,none": 0.42483660130718953,
|
||
|
|
"acc_stderr,none": 0.01999797303545833
|
||
|
|
},
|
||
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|
"openaimmlu_virology": {
|
||
|
|
"alias": " - virology",
|
||
|
|
"acc,none": 0.43373493975903615,
|
||
|
|
"acc_stderr,none": 0.03858158940685517
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||
|
|
},
|
||
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"openaimmlu_social_science": {
|
||
|
|
"acc,none": 0.46682897139379187,
|
||
|
|
"acc_stderr,none": 0.008294155824875415,
|
||
|
|
"alias": " - Social Science"
|
||
|
|
},
|
||
|
|
"openaimmlu_business_ethics": {
|
||
|
|
"alias": " - business_ethics",
|
||
|
|
"acc,none": 0.49,
|
||
|
|
"acc_stderr,none": 0.05024183937956912
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_government_and_politics": {
|
||
|
|
"alias": " - high_school_government_and_politics",
|
||
|
|
"acc,none": 0.6373056994818653,
|
||
|
|
"acc_stderr,none": 0.03469713791704371
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_macroeconomics": {
|
||
|
|
"alias": " - high_school_macroeconomics",
|
||
|
|
"acc,none": 0.4512820512820513,
|
||
|
|
"acc_stderr,none": 0.02523038123893484
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_microeconomics": {
|
||
|
|
"alias": " - high_school_microeconomics",
|
||
|
|
"acc,none": 0.44537815126050423,
|
||
|
|
"acc_stderr,none": 0.0322841062671639
|
||
|
|
},
|
||
|
|
"openaimmlu_human_sexuality": {
|
||
|
|
"alias": " - human_sexuality",
|
||
|
|
"acc,none": 0.5114503816793893,
|
||
|
|
"acc_stderr,none": 0.043841400240780176
|
||
|
|
},
|
||
|
|
"openaimmlu_management": {
|
||
|
|
"alias": " - management",
|
||
|
|
"acc,none": 0.5436893203883495,
|
||
|
|
"acc_stderr,none": 0.049318019942204146
|
||
|
|
},
|
||
|
|
"openaimmlu_marketing": {
|
||
|
|
"alias": " - marketing",
|
||
|
|
"acc,none": 0.6410256410256411,
|
||
|
|
"acc_stderr,none": 0.03142616993791924
|
||
|
|
},
|
||
|
|
"openaimmlu_moral_disputes": {
|
||
|
|
"alias": " - moral_disputes",
|
||
|
|
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|
||
|
|
"acc_stderr,none": 0.026911898686377913
|
||
|
|
},
|
||
|
|
"openaimmlu_moral_scenarios": {
|
||
|
|
"alias": " - moral_scenarios",
|
||
|
|
"acc,none": 0.24692737430167597,
|
||
|
|
"acc_stderr,none": 0.01442229220480885
|
||
|
|
},
|
||
|
|
"openaimmlu_public_relations": {
|
||
|
|
"alias": " - public_relations",
|
||
|
|
"acc,none": 0.5727272727272728,
|
||
|
|
"acc_stderr,none": 0.04738198703545483
|
||
|
|
},
|
||
|
|
"openaimmlu_security_studies": {
|
||
|
|
"alias": " - security_studies",
|
||
|
|
"acc,none": 0.5918367346938775,
|
||
|
|
"acc_stderr,none": 0.03146465712827424
|
||
|
|
},
|
||
|
|
"openaimmlu_sociology": {
|
||
|
|
"alias": " - sociology",
|
||
|
|
"acc,none": 0.7064676616915423,
|
||
|
|
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|
||
|
|
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|
||
|
|
"openaimmlu_us_foreign_policy": {
|
||
|
|
"alias": " - us_foreign_policy",
|
||
|
|
"acc,none": 0.67,
|
||
|
|
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|
||
|
|
}
|
||
|
|
},
|
||
|
|
"groups": {
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
"alias": "openaimmlu"
|
||
|
|
},
|
||
|
|
"openaimmlu_STEM": {
|
||
|
|
"acc,none": 0.4198675496688742,
|
||
|
|
"acc_stderr,none": 0.008819083118680756,
|
||
|
|
"alias": " - STEM"
|
||
|
|
},
|
||
|
|
"openaimmlu_humanities": {
|
||
|
|
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|
||
|
|
"acc_stderr,none": 0.011582619725483814,
|
||
|
|
"alias": " - Humanities"
|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
"acc_stderr,none": 0.0063302986349148774,
|
||
|
|
"alias": " - Other"
|
||
|
|
},
|
||
|
|
"openaimmlu_social_science": {
|
||
|
|
"acc,none": 0.46682897139379187,
|
||
|
|
"acc_stderr,none": 0.008294155824875415,
|
||
|
|
"alias": " - Social Science"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"group_subtasks": {
|
||
|
|
"openaimmlu_humanities": [
|
||
|
|
"openaimmlu_logical_fallacies",
|
||
|
|
"openaimmlu_high_school_us_history",
|
||
|
|
"openaimmlu_prehistory",
|
||
|
|
"openaimmlu_high_school_world_history",
|
||
|
|
"openaimmlu_philosophy",
|
||
|
|
"openaimmlu_international_law",
|
||
|
|
"openaimmlu_jurisprudence",
|
||
|
|
"openaimmlu_world_religions",
|
||
|
|
"openaimmlu_high_school_european_history"
|
||
|
|
],
|
||
|
|
"openaimmlu_social_science": [
|
||
|
|
"openaimmlu_marketing",
|
||
|
|
"openaimmlu_moral_scenarios",
|
||
|
|
"openaimmlu_high_school_macroeconomics",
|
||
|
|
"openaimmlu_high_school_government_and_politics",
|
||
|
|
"openaimmlu_business_ethics",
|
||
|
|
"openaimmlu_high_school_microeconomics",
|
||
|
|
"openaimmlu_security_studies",
|
||
|
|
"openaimmlu_moral_disputes",
|
||
|
|
"openaimmlu_public_relations",
|
||
|
|
"openaimmlu_us_foreign_policy",
|
||
|
|
"openaimmlu_management",
|
||
|
|
"openaimmlu_sociology",
|
||
|
|
"openaimmlu_human_sexuality"
|
||
|
|
],
|
||
|
|
"openaimmlu_other": [
|
||
|
|
"openaimmlu_professional_law",
|
||
|
|
"openaimmlu_medical_genetics",
|
||
|
|
"openaimmlu_nutrition",
|
||
|
|
"openaimmlu_miscellaneous",
|
||
|
|
"openaimmlu_formal_logic",
|
||
|
|
"openaimmlu_high_school_geography",
|
||
|
|
"openaimmlu_professional_medicine",
|
||
|
|
"openaimmlu_clinical_knowledge",
|
||
|
|
"openaimmlu_professional_accounting",
|
||
|
|
"openaimmlu_professional_psychology",
|
||
|
|
"openaimmlu_college_medicine",
|
||
|
|
"openaimmlu_human_aging",
|
||
|
|
"openaimmlu_high_school_psychology",
|
||
|
|
"openaimmlu_anatomy",
|
||
|
|
"openaimmlu_global_facts",
|
||
|
|
"openaimmlu_machine_learning",
|
||
|
|
"openaimmlu_virology"
|
||
|
|
],
|
||
|
|
"openaimmlu_STEM": [
|
||
|
|
"openaimmlu_high_school_physics",
|
||
|
|
"openaimmlu_college_biology",
|
||
|
|
"openaimmlu_computer_security",
|
||
|
|
"openaimmlu_electrical_engineering",
|
||
|
|
"openaimmlu_college_computer_science",
|
||
|
|
"openaimmlu_abstract_algebra",
|
||
|
|
"openaimmlu_high_school_chemistry",
|
||
|
|
"openaimmlu_high_school_biology",
|
||
|
|
"openaimmlu_high_school_mathematics",
|
||
|
|
"openaimmlu_high_school_statistics",
|
||
|
|
"openaimmlu_elementary_mathematics",
|
||
|
|
"openaimmlu_college_mathematics",
|
||
|
|
"openaimmlu_college_physics",
|
||
|
|
"openaimmlu_astronomy",
|
||
|
|
"openaimmlu_college_chemistry",
|
||
|
|
"openaimmlu_econometrics",
|
||
|
|
"openaimmlu_high_school_computer_science",
|
||
|
|
"openaimmlu_conceptual_physics"
|
||
|
|
],
|
||
|
|
"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
|
||
|
|
},
|
||
|
|
"openaimmlu_business_ethics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_clinical_knowledge": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_college_biology": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_college_chemistry": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_college_computer_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_college_mathematics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_college_medicine": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_college_physics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_computer_security": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_conceptual_physics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_econometrics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_electrical_engineering": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_elementary_mathematics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_formal_logic": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_global_facts": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_biology": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_chemistry": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_computer_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_european_history": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_geography": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_government_and_politics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_macroeconomics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_mathematics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_microeconomics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_physics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_psychology": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_statistics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_us_history": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_world_history": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_human_aging": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_human_sexuality": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_humanities": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_international_law": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_jurisprudence": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_logical_fallacies": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_machine_learning": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_management": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_marketing": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_medical_genetics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_miscellaneous": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_moral_disputes": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_moral_scenarios": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_nutrition": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_other": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_philosophy": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_prehistory": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_accounting": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_law": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_medicine": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_psychology": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_public_relations": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_security_studies": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_social_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_sociology": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_us_foreign_policy": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_virology": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_world_religions": {
|
||
|
|
"acc": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"n-samples": {
|
||
|
|
"openaimmlu_high_school_physics": {
|
||
|
|
"original": 151,
|
||
|
|
"effective": 151
|
||
|
|
},
|
||
|
|
"openaimmlu_college_biology": {
|
||
|
|
"original": 144,
|
||
|
|
"effective": 144
|
||
|
|
},
|
||
|
|
"openaimmlu_computer_security": {
|
||
|
|
"original": 100,
|
||
|
|
"effective": 100
|
||
|
|
},
|
||
|
|
"openaimmlu_electrical_engineering": {
|
||
|
|
"original": 145,
|
||
|
|
"effective": 145
|
||
|
|
},
|
||
|
|
"openaimmlu_college_computer_science": {
|
||
|
|
"original": 100,
|
||
|
|
"effective": 100
|
||
|
|
},
|
||
|
|
"openaimmlu_abstract_algebra": {
|
||
|
|
"original": 100,
|
||
|
|
"effective": 100
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_chemistry": {
|
||
|
|
"original": 203,
|
||
|
|
"effective": 203
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_biology": {
|
||
|
|
"original": 310,
|
||
|
|
"effective": 310
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_mathematics": {
|
||
|
|
"original": 270,
|
||
|
|
"effective": 270
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_statistics": {
|
||
|
|
"original": 216,
|
||
|
|
"effective": 216
|
||
|
|
},
|
||
|
|
"openaimmlu_elementary_mathematics": {
|
||
|
|
"original": 378,
|
||
|
|
"effective": 378
|
||
|
|
},
|
||
|
|
"openaimmlu_college_mathematics": {
|
||
|
|
"original": 100,
|
||
|
|
"effective": 100
|
||
|
|
},
|
||
|
|
"openaimmlu_college_physics": {
|
||
|
|
"original": 102,
|
||
|
|
"effective": 102
|
||
|
|
},
|
||
|
|
"openaimmlu_astronomy": {
|
||
|
|
"original": 152,
|
||
|
|
"effective": 152
|
||
|
|
},
|
||
|
|
"openaimmlu_college_chemistry": {
|
||
|
|
"original": 100,
|
||
|
|
"effective": 100
|
||
|
|
},
|
||
|
|
"openaimmlu_econometrics": {
|
||
|
|
"original": 114,
|
||
|
|
"effective": 114
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_computer_science": {
|
||
|
|
"original": 100,
|
||
|
|
"effective": 100
|
||
|
|
},
|
||
|
|
"openaimmlu_conceptual_physics": {
|
||
|
|
"original": 235,
|
||
|
|
"effective": 235
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_law": {
|
||
|
|
"original": 1534,
|
||
|
|
"effective": 1534
|
||
|
|
},
|
||
|
|
"openaimmlu_medical_genetics": {
|
||
|
|
"original": 100,
|
||
|
|
"effective": 100
|
||
|
|
},
|
||
|
|
"openaimmlu_nutrition": {
|
||
|
|
"original": 306,
|
||
|
|
"effective": 306
|
||
|
|
},
|
||
|
|
"openaimmlu_miscellaneous": {
|
||
|
|
"original": 783,
|
||
|
|
"effective": 783
|
||
|
|
},
|
||
|
|
"openaimmlu_formal_logic": {
|
||
|
|
"original": 126,
|
||
|
|
"effective": 126
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_geography": {
|
||
|
|
"original": 198,
|
||
|
|
"effective": 198
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_medicine": {
|
||
|
|
"original": 272,
|
||
|
|
"effective": 272
|
||
|
|
},
|
||
|
|
"openaimmlu_clinical_knowledge": {
|
||
|
|
"original": 265,
|
||
|
|
"effective": 265
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_accounting": {
|
||
|
|
"original": 282,
|
||
|
|
"effective": 282
|
||
|
|
},
|
||
|
|
"openaimmlu_professional_psychology": {
|
||
|
|
"original": 612,
|
||
|
|
"effective": 612
|
||
|
|
},
|
||
|
|
"openaimmlu_college_medicine": {
|
||
|
|
"original": 173,
|
||
|
|
"effective": 173
|
||
|
|
},
|
||
|
|
"openaimmlu_human_aging": {
|
||
|
|
"original": 223,
|
||
|
|
"effective": 223
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_psychology": {
|
||
|
|
"original": 545,
|
||
|
|
"effective": 545
|
||
|
|
},
|
||
|
|
"openaimmlu_anatomy": {
|
||
|
|
"original": 135,
|
||
|
|
"effective": 135
|
||
|
|
},
|
||
|
|
"openaimmlu_global_facts": {
|
||
|
|
"original": 100,
|
||
|
|
"effective": 100
|
||
|
|
},
|
||
|
|
"openaimmlu_machine_learning": {
|
||
|
|
"original": 112,
|
||
|
|
"effective": 112
|
||
|
|
},
|
||
|
|
"openaimmlu_virology": {
|
||
|
|
"original": 166,
|
||
|
|
"effective": 166
|
||
|
|
},
|
||
|
|
"openaimmlu_marketing": {
|
||
|
|
"original": 234,
|
||
|
|
"effective": 234
|
||
|
|
},
|
||
|
|
"openaimmlu_moral_scenarios": {
|
||
|
|
"original": 895,
|
||
|
|
"effective": 895
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_macroeconomics": {
|
||
|
|
"original": 390,
|
||
|
|
"effective": 390
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_government_and_politics": {
|
||
|
|
"original": 193,
|
||
|
|
"effective": 193
|
||
|
|
},
|
||
|
|
"openaimmlu_business_ethics": {
|
||
|
|
"original": 100,
|
||
|
|
"effective": 100
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_microeconomics": {
|
||
|
|
"original": 238,
|
||
|
|
"effective": 238
|
||
|
|
},
|
||
|
|
"openaimmlu_security_studies": {
|
||
|
|
"original": 245,
|
||
|
|
"effective": 245
|
||
|
|
},
|
||
|
|
"openaimmlu_moral_disputes": {
|
||
|
|
"original": 346,
|
||
|
|
"effective": 346
|
||
|
|
},
|
||
|
|
"openaimmlu_public_relations": {
|
||
|
|
"original": 110,
|
||
|
|
"effective": 110
|
||
|
|
},
|
||
|
|
"openaimmlu_us_foreign_policy": {
|
||
|
|
"original": 100,
|
||
|
|
"effective": 100
|
||
|
|
},
|
||
|
|
"openaimmlu_management": {
|
||
|
|
"original": 103,
|
||
|
|
"effective": 103
|
||
|
|
},
|
||
|
|
"openaimmlu_sociology": {
|
||
|
|
"original": 201,
|
||
|
|
"effective": 201
|
||
|
|
},
|
||
|
|
"openaimmlu_human_sexuality": {
|
||
|
|
"original": 131,
|
||
|
|
"effective": 131
|
||
|
|
},
|
||
|
|
"openaimmlu_logical_fallacies": {
|
||
|
|
"original": 163,
|
||
|
|
"effective": 163
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_us_history": {
|
||
|
|
"original": 204,
|
||
|
|
"effective": 204
|
||
|
|
},
|
||
|
|
"openaimmlu_prehistory": {
|
||
|
|
"original": 324,
|
||
|
|
"effective": 324
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_world_history": {
|
||
|
|
"original": 237,
|
||
|
|
"effective": 237
|
||
|
|
},
|
||
|
|
"openaimmlu_philosophy": {
|
||
|
|
"original": 311,
|
||
|
|
"effective": 311
|
||
|
|
},
|
||
|
|
"openaimmlu_international_law": {
|
||
|
|
"original": 121,
|
||
|
|
"effective": 121
|
||
|
|
},
|
||
|
|
"openaimmlu_jurisprudence": {
|
||
|
|
"original": 108,
|
||
|
|
"effective": 108
|
||
|
|
},
|
||
|
|
"openaimmlu_world_religions": {
|
||
|
|
"original": 171,
|
||
|
|
"effective": 171
|
||
|
|
},
|
||
|
|
"openaimmlu_high_school_european_history": {
|
||
|
|
"original": 165,
|
||
|
|
"effective": 165
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"config": {
|
||
|
|
"model": "hf",
|
||
|
|
"model_args": "pretrained=mistralai/Mistral-Nemo-Instruct-2407,trust_remote_code=True,cache_dir=/tmp,parallelize=False",
|
||
|
|
"model_num_parameters": 12247782400,
|
||
|
|
"model_dtype": "torch.bfloat16",
|
||
|
|
"model_revision": "main",
|
||
|
|
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"batch_sizes": [
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32
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"gen_kwargs": null,
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},
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"git_hash": "5e10e017",
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"date": 1736969874.3072467,
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"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 48\nOn-line CPU(s) list: 0-47\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V13 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4890.89\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 24 MiB (48 instances)\nL3 cache: 192 MiB (6 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.
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"transformers_version": "4.48.0",
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"0"
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],
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"tokenizer_eos_token": [
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"</s>",
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"2"
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],
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"tokenizer_bos_token": [
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"<s>",
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],
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"task_hashes": {},
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"model_source": "hf",
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"model_name": "mistralai/Mistral-Nemo-Instruct-2407",
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"model_name_sanitized": "mistralai__Mistral-Nemo-Instruct-2407",
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"system_instruction": null,
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"chat_template": null,
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"chat_template_sha": null,
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"total_evaluation_time_seconds": "283.70783782800027"
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}
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