2051 lines
99 KiB
JSON
2051 lines
99 KiB
JSON
{
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|
"results": {
|
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"arabicmmlu": {
|
|
"acc,none": 0.5043237634036666,
|
|
"acc_stderr,none": 0.004042363470895757,
|
|
"alias": "arabicmmlu"
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},
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|
"arabicmmlu_humanities": {
|
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"acc,none": 0.5052370452039692,
|
|
"acc_stderr,none": 0.00790960602679391,
|
|
"alias": " - Humanities"
|
|
},
|
|
"arabicmmlu_high_history": {
|
|
"alias": " - High History",
|
|
"acc,none": 0.3368421052631579,
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|
"acc_stderr,none": 0.017155396919294835
|
|
},
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|
"arabicmmlu_high_islamic_studies": {
|
|
"alias": " - High Islamic Studies",
|
|
"acc,none": 0.6407185628742516,
|
|
"acc_stderr,none": 0.026292321014549997
|
|
},
|
|
"arabicmmlu_high_philosophy": {
|
|
"alias": " - High Philosophy",
|
|
"acc,none": 0.48717948717948717,
|
|
"acc_stderr,none": 0.08108404256842
|
|
},
|
|
"arabicmmlu_islamic_studies": {
|
|
"alias": " - Islamic Studies",
|
|
"acc,none": 0.3317683881064163,
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|
"acc_stderr,none": 0.018641062838831428
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},
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"arabicmmlu_middle_history": {
|
|
"alias": " - Middle History",
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|
"acc,none": 0.49261083743842365,
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"acc_stderr,none": 0.035176035403610084
|
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},
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|
"arabicmmlu_middle_islamic_studies": {
|
|
"alias": " - Middle Islamic Studies",
|
|
"acc,none": 0.6134453781512605,
|
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"acc_stderr,none": 0.03163145807552378
|
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},
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|
"arabicmmlu_primary_history": {
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"alias": " - Primary History",
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"acc,none": 0.46078431372549017,
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"acc_stderr,none": 0.04959859966384181
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},
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"arabicmmlu_primary_islamic_studies": {
|
|
"alias": " - Primary Islamic Studies",
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"acc,none": 0.6926926926926927,
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|
"acc_stderr,none": 0.014604660845760144
|
|
},
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|
"arabicmmlu_prof_law": {
|
|
"alias": " - Prof Law",
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|
"acc,none": 0.4681528662420382,
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|
"acc_stderr,none": 0.028204284454138768
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},
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|
"arabicmmlu_language": {
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"acc,none": 0.4775212636695018,
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"acc_stderr,none": 0.012004811696820014,
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"alias": " - Language"
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|
},
|
|
"arabicmmlu_arabic_language_(general)": {
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"alias": " - Arabic Language (General)",
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"acc,none": 0.5980392156862745,
|
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"acc_stderr,none": 0.01983517648437538
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},
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"arabicmmlu_arabic_language_(grammar)": {
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"alias": " - Arabic Language (Grammar)",
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"acc,none": 0.34794520547945207,
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"acc_stderr,none": 0.024965874481689576
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},
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"arabicmmlu_high_arabic_language": {
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"alias": " - High Arabic Language",
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|
"acc,none": 0.3641025641025641,
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"acc_stderr,none": 0.02439667298509477
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},
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"arabicmmlu_middle_arabic_language": {
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"alias": " - Middle Arabic Language",
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"acc,none": 0.6296296296296297,
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"acc_stderr,none": 0.09470524295495535
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},
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"arabicmmlu_primary_arabic_language": {
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"alias": " - Primary Arabic Language",
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"acc,none": 0.5317460317460317,
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"acc_stderr,none": 0.03149604347936578
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},
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"arabicmmlu_other": {
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"acc,none": 0.5628019323671497,
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"acc_stderr,none": 0.009820739967892693,
|
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"alias": " - Other"
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|
},
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"arabicmmlu_driving_test": {
|
|
"alias": " - Driving Test",
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"acc,none": 0.620148637489678,
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"acc_stderr,none": 0.01395282207034666
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},
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"arabicmmlu_general_knowledge": {
|
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"alias": " - General Knowledge",
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"acc,none": 0.45023148148148145,
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"acc_stderr,none": 0.016935673216772293
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},
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"arabicmmlu_middle_general_knowledge": {
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"alias": " - Middle General Knowledge",
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"acc,none": 0.5930232558139535,
|
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"acc_stderr,none": 0.03756839173779933
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},
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"arabicmmlu_primary_general_knowledge": {
|
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"alias": " - Primary General Knowledge",
|
|
"acc,none": 0.6481481481481481,
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"acc_stderr,none": 0.037636057624863876
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},
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|
"arabicmmlu_univ_management": {
|
|
"alias": " - Univ Management",
|
|
"acc,none": 0.68,
|
|
"acc_stderr,none": 0.05422675115236518
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|
},
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|
"arabicmmlu_social_science": {
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"acc,none": 0.4994292237442922,
|
|
"acc_stderr,none": 0.008286856287550251,
|
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"alias": " - Social Science"
|
|
},
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"arabicmmlu_high_civics": {
|
|
"alias": " - High Civics",
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"acc,none": 0.42528735632183906,
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|
"acc_stderr,none": 0.05331106836455265
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},
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"arabicmmlu_high_economics": {
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|
"alias": " - High Economics",
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|
"acc,none": 0.5222222222222223,
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"acc_stderr,none": 0.026362914614329245
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},
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|
"arabicmmlu_high_geography": {
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"alias": " - High Geography",
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|
"acc,none": 0.3988439306358382,
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"acc_stderr,none": 0.015205676046200057
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},
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"arabicmmlu_middle_civics": {
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"alias": " - Middle Civics",
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"acc,none": 0.3686440677966102,
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"acc_stderr,none": 0.0314707306823461
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},
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"arabicmmlu_middle_economics": {
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"alias": " - Middle Economics",
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"acc,none": 0.6551724137931034,
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"acc_stderr,none": 0.05125421389342353
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},
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"arabicmmlu_middle_geography": {
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"alias": " - Middle Geography",
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"acc,none": 0.5698529411764706,
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"acc_stderr,none": 0.030074971917302875
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},
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"arabicmmlu_middle_social_science": {
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"alias": " - Middle Social Science",
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"acc,none": 0.4854771784232365,
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"acc_stderr,none": 0.03226124401232391
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},
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|
"arabicmmlu_primary_geography": {
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"alias": " - Primary Geography",
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"acc,none": 0.543859649122807,
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"acc_stderr,none": 0.0665577530069649
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},
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"arabicmmlu_primary_social_science": {
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"alias": " - Primary Social Science",
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"acc,none": 0.6524822695035462,
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"acc_stderr,none": 0.017946778859462872
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},
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"arabicmmlu_univ_accounting": {
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"alias": " - Univ Accounting",
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"acc,none": 0.5405405405405406,
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"acc_stderr,none": 0.05832789513012364
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},
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"arabicmmlu_univ_economics": {
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"alias": " - Univ Economics",
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"acc,none": 0.48175182481751827,
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"acc_stderr,none": 0.04284608260823147
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},
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"arabicmmlu_univ_political_science": {
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"alias": " - Univ Political Science",
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"acc,none": 0.4666666666666667,
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"acc_stderr,none": 0.034508780443504965
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},
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"arabicmmlu_stem": {
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"acc,none": 0.47698089570936425,
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"acc_stderr,none": 0.008646289649970346,
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"alias": " - STEM"
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},
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"arabicmmlu_high_biology": {
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"alias": " - High Biology",
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"acc,none": 0.38892831795599714,
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"acc_stderr,none": 0.012992105378448731
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},
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"arabicmmlu_high_computer_science": {
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"alias": " - High Computer Science",
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"acc,none": 0.49808429118773945,
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"acc_stderr,none": 0.031008456046434162
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},
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|
"arabicmmlu_high_physics": {
|
|
"alias": " - High Physics",
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|
"acc,none": 0.3803921568627451,
|
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"acc_stderr,none": 0.03046192691828629
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},
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"arabicmmlu_middle_computer_science": {
|
|
"alias": " - Middle Computer Science",
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|
"acc,none": 0.5555555555555556,
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"acc_stderr,none": 0.09745089103411436
|
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},
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"arabicmmlu_middle_natural_science": {
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"alias": " - Middle Natural Science",
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|
"acc,none": 0.5495867768595041,
|
|
"acc_stderr,none": 0.03204905158847432
|
|
},
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"arabicmmlu_primary_computer_science": {
|
|
"alias": " - Primary Computer Science",
|
|
"acc,none": 0.7157894736842105,
|
|
"acc_stderr,none": 0.03280815673574656
|
|
},
|
|
"arabicmmlu_primary_math": {
|
|
"alias": " - Primary Math",
|
|
"acc,none": 0.5232273838630807,
|
|
"acc_stderr,none": 0.02472696435617918
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|
},
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|
"arabicmmlu_primary_natural_science": {
|
|
"alias": " - Primary Natural Science",
|
|
"acc,none": 0.6488095238095238,
|
|
"acc_stderr,none": 0.02607999894833243
|
|
},
|
|
"arabicmmlu_univ_computer_science": {
|
|
"alias": " - Univ Computer Science",
|
|
"acc,none": 0.5,
|
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"acc_stderr,none": 0.06299407883487121
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}
|
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},
|
|
"groups": {
|
|
"arabicmmlu": {
|
|
"acc,none": 0.5043237634036666,
|
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"acc_stderr,none": 0.004042363470895757,
|
|
"alias": "arabicmmlu"
|
|
},
|
|
"arabicmmlu_humanities": {
|
|
"acc,none": 0.5052370452039692,
|
|
"acc_stderr,none": 0.00790960602679391,
|
|
"alias": " - Humanities"
|
|
},
|
|
"arabicmmlu_language": {
|
|
"acc,none": 0.4775212636695018,
|
|
"acc_stderr,none": 0.012004811696820014,
|
|
"alias": " - Language"
|
|
},
|
|
"arabicmmlu_other": {
|
|
"acc,none": 0.5628019323671497,
|
|
"acc_stderr,none": 0.009820739967892693,
|
|
"alias": " - Other"
|
|
},
|
|
"arabicmmlu_social_science": {
|
|
"acc,none": 0.4994292237442922,
|
|
"acc_stderr,none": 0.008286856287550251,
|
|
"alias": " - Social Science"
|
|
},
|
|
"arabicmmlu_stem": {
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|
"acc,none": 0.47698089570936425,
|
|
"acc_stderr,none": 0.008646289649970346,
|
|
"alias": " - STEM"
|
|
}
|
|
},
|
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"group_subtasks": {
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"arabicmmlu_language": [
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"arabicmmlu_primary_arabic_language",
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"arabicmmlu_arabic_language_(grammar)",
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"arabicmmlu_high_arabic_language",
|
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"arabicmmlu_middle_arabic_language",
|
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"arabicmmlu_arabic_language_(general)"
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|
],
|
|
"arabicmmlu_stem": [
|
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"arabicmmlu_middle_natural_science",
|
|
"arabicmmlu_univ_computer_science",
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"arabicmmlu_high_physics",
|
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"arabicmmlu_high_biology",
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"arabicmmlu_middle_computer_science",
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"arabicmmlu_primary_natural_science",
|
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"arabicmmlu_primary_computer_science",
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"arabicmmlu_high_computer_science",
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"arabicmmlu_primary_math"
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],
|
|
"arabicmmlu_humanities": [
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"arabicmmlu_middle_islamic_studies",
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"arabicmmlu_prof_law",
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"arabicmmlu_primary_islamic_studies",
|
|
"arabicmmlu_high_history",
|
|
"arabicmmlu_middle_history",
|
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"arabicmmlu_islamic_studies",
|
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"arabicmmlu_high_islamic_studies",
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"arabicmmlu_high_philosophy",
|
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"arabicmmlu_primary_history"
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],
|
|
"arabicmmlu_social_science": [
|
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"arabicmmlu_primary_geography",
|
|
"arabicmmlu_middle_social_science",
|
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"arabicmmlu_high_civics",
|
|
"arabicmmlu_middle_geography",
|
|
"arabicmmlu_primary_social_science",
|
|
"arabicmmlu_middle_economics",
|
|
"arabicmmlu_middle_civics",
|
|
"arabicmmlu_univ_economics",
|
|
"arabicmmlu_univ_accounting",
|
|
"arabicmmlu_univ_political_science",
|
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"arabicmmlu_high_geography",
|
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"arabicmmlu_high_economics"
|
|
],
|
|
"arabicmmlu_other": [
|
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"arabicmmlu_general_knowledge",
|
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"arabicmmlu_driving_test",
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"arabicmmlu_univ_management",
|
|
"arabicmmlu_primary_general_knowledge",
|
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"arabicmmlu_middle_general_knowledge"
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|
],
|
|
"arabicmmlu": [
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"arabicmmlu_other",
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"arabicmmlu_social_science",
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"arabicmmlu_humanities",
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"arabicmmlu_stem",
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"arabicmmlu_language"
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]
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},
|
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"configs": {
|
|
"arabicmmlu_arabic_language_(general)": {
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"task": "arabicmmlu_arabic_language_(general)",
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"task_alias": "Arabic Language (General)",
|
|
"tag": "arabicmmlu_language_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Arabic Language (General)",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
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|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_arabic_language_(grammar)": {
|
|
"task": "arabicmmlu_arabic_language_(grammar)",
|
|
"task_alias": "Arabic Language (Grammar)",
|
|
"tag": "arabicmmlu_language_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Arabic Language (Grammar)",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_driving_test": {
|
|
"task": "arabicmmlu_driving_test",
|
|
"task_alias": "Driving Test",
|
|
"tag": "arabicmmlu_other_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Driving Test",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_general_knowledge": {
|
|
"task": "arabicmmlu_general_knowledge",
|
|
"task_alias": "General Knowledge",
|
|
"tag": "arabicmmlu_other_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "General Knowledge",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_high_arabic_language": {
|
|
"task": "arabicmmlu_high_arabic_language",
|
|
"task_alias": "High Arabic Language",
|
|
"tag": "arabicmmlu_language_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "High Arabic Language",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_high_biology": {
|
|
"task": "arabicmmlu_high_biology",
|
|
"task_alias": "High Biology",
|
|
"tag": "arabicmmlu_stem_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "High Biology",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_high_civics": {
|
|
"task": "arabicmmlu_high_civics",
|
|
"task_alias": "High Civics",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "High Civics",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_high_computer_science": {
|
|
"task": "arabicmmlu_high_computer_science",
|
|
"task_alias": "High Computer Science",
|
|
"tag": "arabicmmlu_stem_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "High Computer Science",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_high_economics": {
|
|
"task": "arabicmmlu_high_economics",
|
|
"task_alias": "High Economics",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "High Economics",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_high_geography": {
|
|
"task": "arabicmmlu_high_geography",
|
|
"task_alias": "High Geography",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "High Geography",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_high_history": {
|
|
"task": "arabicmmlu_high_history",
|
|
"task_alias": "High History",
|
|
"tag": "arabicmmlu_humanities_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "High History",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_high_islamic_studies": {
|
|
"task": "arabicmmlu_high_islamic_studies",
|
|
"task_alias": "High Islamic Studies",
|
|
"tag": "arabicmmlu_humanities_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "High Islamic Studies",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_high_philosophy": {
|
|
"task": "arabicmmlu_high_philosophy",
|
|
"task_alias": "High Philosophy",
|
|
"tag": "arabicmmlu_humanities_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "High Philosophy",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_high_physics": {
|
|
"task": "arabicmmlu_high_physics",
|
|
"task_alias": "High Physics",
|
|
"tag": "arabicmmlu_stem_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "High Physics",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_islamic_studies": {
|
|
"task": "arabicmmlu_islamic_studies",
|
|
"task_alias": "Islamic Studies",
|
|
"tag": "arabicmmlu_humanities_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Islamic Studies",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_middle_arabic_language": {
|
|
"task": "arabicmmlu_middle_arabic_language",
|
|
"task_alias": "Middle Arabic Language",
|
|
"tag": "arabicmmlu_language_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Middle Arabic Language",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_middle_civics": {
|
|
"task": "arabicmmlu_middle_civics",
|
|
"task_alias": "Middle Civics",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Middle Civics",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_middle_computer_science": {
|
|
"task": "arabicmmlu_middle_computer_science",
|
|
"task_alias": "Middle Computer Science",
|
|
"tag": "arabicmmlu_stem_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Middle Computer Science",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_middle_economics": {
|
|
"task": "arabicmmlu_middle_economics",
|
|
"task_alias": "Middle Economics",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Middle Economics",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_middle_general_knowledge": {
|
|
"task": "arabicmmlu_middle_general_knowledge",
|
|
"task_alias": "Middle General Knowledge",
|
|
"tag": "arabicmmlu_other_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Middle General Knowledge",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_middle_geography": {
|
|
"task": "arabicmmlu_middle_geography",
|
|
"task_alias": "Middle Geography",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Middle Geography",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_middle_history": {
|
|
"task": "arabicmmlu_middle_history",
|
|
"task_alias": "Middle History",
|
|
"tag": "arabicmmlu_humanities_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Middle History",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_middle_islamic_studies": {
|
|
"task": "arabicmmlu_middle_islamic_studies",
|
|
"task_alias": "Middle Islamic Studies",
|
|
"tag": "arabicmmlu_humanities_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Middle Islamic Studies",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_middle_natural_science": {
|
|
"task": "arabicmmlu_middle_natural_science",
|
|
"task_alias": "Middle Natural Science",
|
|
"tag": "arabicmmlu_stem_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Middle Natural Science",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_middle_social_science": {
|
|
"task": "arabicmmlu_middle_social_science",
|
|
"task_alias": "Middle Social Science",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Middle Social Science",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_primary_arabic_language": {
|
|
"task": "arabicmmlu_primary_arabic_language",
|
|
"task_alias": "Primary Arabic Language",
|
|
"tag": "arabicmmlu_language_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Primary Arabic Language",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_primary_computer_science": {
|
|
"task": "arabicmmlu_primary_computer_science",
|
|
"task_alias": "Primary Computer Science",
|
|
"tag": "arabicmmlu_stem_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Primary Computer Science",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_primary_general_knowledge": {
|
|
"task": "arabicmmlu_primary_general_knowledge",
|
|
"task_alias": "Primary General Knowledge",
|
|
"tag": "arabicmmlu_other_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Primary General Knowledge",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_primary_geography": {
|
|
"task": "arabicmmlu_primary_geography",
|
|
"task_alias": "Primary Geography",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Primary Geography",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_primary_history": {
|
|
"task": "arabicmmlu_primary_history",
|
|
"task_alias": "Primary History",
|
|
"tag": "arabicmmlu_humanities_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Primary History",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_primary_islamic_studies": {
|
|
"task": "arabicmmlu_primary_islamic_studies",
|
|
"task_alias": "Primary Islamic Studies",
|
|
"tag": "arabicmmlu_humanities_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Primary Islamic Studies",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_primary_math": {
|
|
"task": "arabicmmlu_primary_math",
|
|
"task_alias": "Primary Math",
|
|
"tag": "arabicmmlu_stem_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Primary Math",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_primary_natural_science": {
|
|
"task": "arabicmmlu_primary_natural_science",
|
|
"task_alias": "Primary Natural Science",
|
|
"tag": "arabicmmlu_stem_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Primary Natural Science",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_primary_social_science": {
|
|
"task": "arabicmmlu_primary_social_science",
|
|
"task_alias": "Primary Social Science",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Primary Social Science",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_prof_law": {
|
|
"task": "arabicmmlu_prof_law",
|
|
"task_alias": "Prof Law",
|
|
"tag": "arabicmmlu_humanities_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Prof Law",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_univ_accounting": {
|
|
"task": "arabicmmlu_univ_accounting",
|
|
"task_alias": "Univ Accounting",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Univ Accounting",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_univ_computer_science": {
|
|
"task": "arabicmmlu_univ_computer_science",
|
|
"task_alias": "Univ Computer Science",
|
|
"tag": "arabicmmlu_stem_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Univ Computer Science",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_univ_economics": {
|
|
"task": "arabicmmlu_univ_economics",
|
|
"task_alias": "Univ Economics",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Univ Economics",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_univ_management": {
|
|
"task": "arabicmmlu_univ_management",
|
|
"task_alias": "Univ Management",
|
|
"tag": "arabicmmlu_other_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Univ Management",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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
|
|
}
|
|
},
|
|
"arabicmmlu_univ_political_science": {
|
|
"task": "arabicmmlu_univ_political_science",
|
|
"task_alias": "Univ Political Science",
|
|
"tag": "arabicmmlu_social_science_tasks",
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
|
"dataset_name": "Univ Political Science",
|
|
"test_split": "test",
|
|
"fewshot_split": "dev",
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
|
"doc_to_target": "Answer Key",
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
|
"description": "",
|
|
"target_delimiter": " ",
|
|
"fewshot_delimiter": "\n\n",
|
|
"fewshot_config": {
|
|
"sampler": "first_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": {
|
|
"arabicmmlu": 0,
|
|
"arabicmmlu_arabic_language_(general)": 0.0,
|
|
"arabicmmlu_arabic_language_(grammar)": 0.0,
|
|
"arabicmmlu_driving_test": 0.0,
|
|
"arabicmmlu_general_knowledge": 0.0,
|
|
"arabicmmlu_high_arabic_language": 0.0,
|
|
"arabicmmlu_high_biology": 0.0,
|
|
"arabicmmlu_high_civics": 0.0,
|
|
"arabicmmlu_high_computer_science": 0.0,
|
|
"arabicmmlu_high_economics": 0.0,
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"config": {
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"model_args": "pretrained=mistralai/Mistral-Small-Instruct-2409,trust_remote_code=True,cache_dir=/tmp,parallelize=False",
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16
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"date": 1736972751.2143774,
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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: 4890.87\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==1.2.0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
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"transformers_version": "4.48.0",
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