2051 lines
99 KiB
JSON
2051 lines
99 KiB
JSON
{
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|
"results": {
|
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"arabicmmlu": {
|
|
"acc,none": 0.6936008301625735,
|
|
"acc_stderr,none": 0.00373302587909067,
|
|
"alias": "arabicmmlu"
|
|
},
|
|
"arabicmmlu_humanities": {
|
|
"acc,none": 0.6827453142227122,
|
|
"acc_stderr,none": 0.007472393741912611,
|
|
"alias": " - Humanities"
|
|
},
|
|
"arabicmmlu_high_history": {
|
|
"alias": " - High History",
|
|
"acc,none": 0.5263157894736842,
|
|
"acc_stderr,none": 0.0181236958723731
|
|
},
|
|
"arabicmmlu_high_islamic_studies": {
|
|
"alias": " - High Islamic Studies",
|
|
"acc,none": 0.7125748502994012,
|
|
"acc_stderr,none": 0.02480021874723033
|
|
},
|
|
"arabicmmlu_high_philosophy": {
|
|
"alias": " - High Philosophy",
|
|
"acc,none": 0.717948717948718,
|
|
"acc_stderr,none": 0.07299934324587597
|
|
},
|
|
"arabicmmlu_islamic_studies": {
|
|
"alias": " - Islamic Studies",
|
|
"acc,none": 0.5743348982785602,
|
|
"acc_stderr,none": 0.01957520354642272
|
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},
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|
"arabicmmlu_middle_history": {
|
|
"alias": " - Middle History",
|
|
"acc,none": 0.7142857142857143,
|
|
"acc_stderr,none": 0.0317852971064275
|
|
},
|
|
"arabicmmlu_middle_islamic_studies": {
|
|
"alias": " - Middle Islamic Studies",
|
|
"acc,none": 0.6974789915966386,
|
|
"acc_stderr,none": 0.029837962388291922
|
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},
|
|
"arabicmmlu_primary_history": {
|
|
"alias": " - Primary History",
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"acc,none": 0.696078431372549,
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|
"acc_stderr,none": 0.045766654032077636
|
|
},
|
|
"arabicmmlu_primary_islamic_studies": {
|
|
"alias": " - Primary Islamic Studies",
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|
"acc,none": 0.8438438438438438,
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|
"acc_stderr,none": 0.011490669345809187
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|
},
|
|
"arabicmmlu_prof_law": {
|
|
"alias": " - Prof Law",
|
|
"acc,none": 0.697452229299363,
|
|
"acc_stderr,none": 0.02596462432074243
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|
},
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|
"arabicmmlu_language": {
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"acc,none": 0.6980558930741191,
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"acc_stderr,none": 0.010952159128929795,
|
|
"alias": " - Language"
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|
},
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|
"arabicmmlu_arabic_language_(general)": {
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"alias": " - Arabic Language (General)",
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|
"acc,none": 0.7973856209150327,
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"acc_stderr,none": 0.01626105528374612
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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": 0.7095890410958904,
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"acc_stderr,none": 0.02379355080761079
|
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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": 0.4948717948717949,
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|
"acc_stderr,none": 0.025349672906838653
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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": 0.7777777777777778,
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"acc_stderr,none": 0.08153326507837146
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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": 0.746031746031746,
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"acc_stderr,none": 0.027474608338697432
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},
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"arabicmmlu_other": {
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"acc,none": 0.7270531400966184,
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"acc_stderr,none": 0.008920558221864296,
|
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"alias": " - Other"
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},
|
|
"arabicmmlu_driving_test": {
|
|
"alias": " - Driving Test",
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|
"acc,none": 0.7563996696944674,
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"acc_stderr,none": 0.012340191989229594
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},
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|
"arabicmmlu_general_knowledge": {
|
|
"alias": " - General Knowledge",
|
|
"acc,none": 0.6828703703703703,
|
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"acc_stderr,none": 0.01584098369286431
|
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},
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|
"arabicmmlu_middle_general_knowledge": {
|
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"alias": " - Middle General Knowledge",
|
|
"acc,none": 0.7151162790697675,
|
|
"acc_stderr,none": 0.0345162887625062
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|
},
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|
"arabicmmlu_primary_general_knowledge": {
|
|
"alias": " - Primary General Knowledge",
|
|
"acc,none": 0.7345679012345679,
|
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"acc_stderr,none": 0.034800041025035575
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|
},
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|
"arabicmmlu_univ_management": {
|
|
"alias": " - Univ Management",
|
|
"acc,none": 0.7733333333333333,
|
|
"acc_stderr,none": 0.04866999865182628
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|
},
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|
"arabicmmlu_social_science": {
|
|
"acc,none": 0.6843607305936074,
|
|
"acc_stderr,none": 0.007708754356580086,
|
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"alias": " - Social Science"
|
|
},
|
|
"arabicmmlu_high_civics": {
|
|
"alias": " - High Civics",
|
|
"acc,none": 0.47126436781609193,
|
|
"acc_stderr,none": 0.05382727149237504
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},
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|
"arabicmmlu_high_economics": {
|
|
"alias": " - High Economics",
|
|
"acc,none": 0.6861111111111111,
|
|
"acc_stderr,none": 0.02449277389433383
|
|
},
|
|
"arabicmmlu_high_geography": {
|
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"alias": " - High Geography",
|
|
"acc,none": 0.6078998073217726,
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|
"acc_stderr,none": 0.015160905911641495
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},
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"arabicmmlu_middle_civics": {
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|
"alias": " - Middle Civics",
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|
"acc,none": 0.6228813559322034,
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"acc_stderr,none": 0.03161605923498462
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},
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"arabicmmlu_middle_economics": {
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|
"alias": " - Middle Economics",
|
|
"acc,none": 0.8045977011494253,
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"acc_stderr,none": 0.04275678110973871
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},
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"arabicmmlu_middle_geography": {
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"alias": " - Middle Geography",
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|
"acc,none": 0.7169117647058824,
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"acc_stderr,none": 0.02736586113151381
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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": 0.6265560165975104,
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"acc_stderr,none": 0.03122389407322075
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},
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"arabicmmlu_primary_geography": {
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"alias": " - Primary Geography",
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"acc,none": 0.8245614035087719,
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"acc_stderr,none": 0.05082531275857955
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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": 0.8297872340425532,
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"acc_stderr,none": 0.014164234541466977
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},
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"arabicmmlu_univ_accounting": {
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"alias": " - Univ Accounting",
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"acc,none": 0.7297297297297297,
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"acc_stderr,none": 0.05197789984508372
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},
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"arabicmmlu_univ_economics": {
|
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"alias": " - Univ Economics",
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|
"acc,none": 0.635036496350365,
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"acc_stderr,none": 0.041281418039994466
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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": 0.680952380952381,
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"acc_stderr,none": 0.03224133248962465
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},
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"arabicmmlu_stem": {
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"acc,none": 0.6877544628875666,
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"acc_stderr,none": 0.0078686460877362,
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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": 0.5592618878637331,
|
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"acc_stderr,none": 0.013231119391259417
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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": 0.7279693486590039,
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"acc_stderr,none": 0.027598075188734354
|
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},
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|
"arabicmmlu_high_physics": {
|
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"alias": " - High Physics",
|
|
"acc,none": 0.6,
|
|
"acc_stderr,none": 0.030738931174713525
|
|
},
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|
"arabicmmlu_middle_computer_science": {
|
|
"alias": " - Middle Computer Science",
|
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"acc,none": 0.9629629629629629,
|
|
"acc_stderr,none": 0.037037037037037035
|
|
},
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|
"arabicmmlu_middle_natural_science": {
|
|
"alias": " - Middle Natural Science",
|
|
"acc,none": 0.8471074380165289,
|
|
"acc_stderr,none": 0.0231821603389708
|
|
},
|
|
"arabicmmlu_primary_computer_science": {
|
|
"alias": " - Primary Computer Science",
|
|
"acc,none": 0.8,
|
|
"acc_stderr,none": 0.02909571869813228
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|
},
|
|
"arabicmmlu_primary_math": {
|
|
"alias": " - Primary Math",
|
|
"acc,none": 0.823960880195599,
|
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"acc_stderr,none": 0.018855055239784486
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|
},
|
|
"arabicmmlu_primary_natural_science": {
|
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"alias": " - Primary Natural Science",
|
|
"acc,none": 0.8720238095238095,
|
|
"acc_stderr,none": 0.018251827563156547
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|
},
|
|
"arabicmmlu_univ_computer_science": {
|
|
"alias": " - Univ Computer Science",
|
|
"acc,none": 0.8125,
|
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"acc_stderr,none": 0.0491747370293402
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}
|
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},
|
|
"groups": {
|
|
"arabicmmlu": {
|
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"acc,none": 0.6936008301625735,
|
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"acc_stderr,none": 0.00373302587909067,
|
|
"alias": "arabicmmlu"
|
|
},
|
|
"arabicmmlu_humanities": {
|
|
"acc,none": 0.6827453142227122,
|
|
"acc_stderr,none": 0.007472393741912611,
|
|
"alias": " - Humanities"
|
|
},
|
|
"arabicmmlu_language": {
|
|
"acc,none": 0.6980558930741191,
|
|
"acc_stderr,none": 0.010952159128929795,
|
|
"alias": " - Language"
|
|
},
|
|
"arabicmmlu_other": {
|
|
"acc,none": 0.7270531400966184,
|
|
"acc_stderr,none": 0.008920558221864296,
|
|
"alias": " - Other"
|
|
},
|
|
"arabicmmlu_social_science": {
|
|
"acc,none": 0.6843607305936074,
|
|
"acc_stderr,none": 0.007708754356580086,
|
|
"alias": " - Social Science"
|
|
},
|
|
"arabicmmlu_stem": {
|
|
"acc,none": 0.6877544628875666,
|
|
"acc_stderr,none": 0.0078686460877362,
|
|
"alias": " - STEM"
|
|
}
|
|
},
|
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"group_subtasks": {
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"arabicmmlu_language": [
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"arabicmmlu_arabic_language_(grammar)",
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"arabicmmlu_middle_arabic_language",
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"arabicmmlu_high_arabic_language",
|
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"arabicmmlu_primary_arabic_language",
|
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"arabicmmlu_arabic_language_(general)"
|
|
],
|
|
"arabicmmlu_stem": [
|
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"arabicmmlu_primary_computer_science",
|
|
"arabicmmlu_univ_computer_science",
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"arabicmmlu_middle_natural_science",
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"arabicmmlu_high_physics",
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"arabicmmlu_primary_math",
|
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"arabicmmlu_primary_natural_science",
|
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"arabicmmlu_high_biology",
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"arabicmmlu_middle_computer_science",
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"arabicmmlu_high_computer_science"
|
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],
|
|
"arabicmmlu_humanities": [
|
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"arabicmmlu_middle_islamic_studies",
|
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"arabicmmlu_high_history",
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"arabicmmlu_islamic_studies",
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|
"arabicmmlu_high_philosophy",
|
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"arabicmmlu_prof_law",
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"arabicmmlu_high_islamic_studies",
|
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"arabicmmlu_primary_islamic_studies",
|
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"arabicmmlu_primary_history",
|
|
"arabicmmlu_middle_history"
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],
|
|
"arabicmmlu_social_science": [
|
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"arabicmmlu_primary_geography",
|
|
"arabicmmlu_middle_economics",
|
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"arabicmmlu_univ_political_science",
|
|
"arabicmmlu_primary_social_science",
|
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"arabicmmlu_middle_civics",
|
|
"arabicmmlu_high_civics",
|
|
"arabicmmlu_middle_geography",
|
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"arabicmmlu_univ_economics",
|
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"arabicmmlu_univ_accounting",
|
|
"arabicmmlu_high_geography",
|
|
"arabicmmlu_high_economics",
|
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"arabicmmlu_middle_social_science"
|
|
],
|
|
"arabicmmlu_other": [
|
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"arabicmmlu_middle_general_knowledge",
|
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"arabicmmlu_driving_test",
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"arabicmmlu_univ_management",
|
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"arabicmmlu_general_knowledge",
|
|
"arabicmmlu_primary_general_knowledge"
|
|
],
|
|
"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",
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|
"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": "hf",
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"model_args": "pretrained=Qwen/Qwen2.5-14B-Instruct,trust_remote_code=True,cache_dir=/tmp,parallelize=False",
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"date": 1736972201.2878518,
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"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 48\nOn-line CPU(s) list: 0-47\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V13 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4890.89\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 24 MiB (48 instances)\nL3 cache: 192 MiB (6 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.0\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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