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ALLaM-7B-Instruct-preview/evaluations/ar/AceGPT-v2-8B-Chat/openaimmlu_0_shot.json
ModelHub XC 3e4c694337 初始化项目，由ModelHub XC社区提供模型 
Model: lanawwas/ALLaM-7B-Instruct-preview
Source: Original Platform
2026-04-22 10:54:04 +08:00

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{
"results": {
"openaimmlu": {
"acc,none": 0.49992878507335137,
"acc_stderr,none": 0.004078575700822945,
"alias": "openaimmlu"
},
"openaimmlu_STEM": {
"acc,none": 0.41456953642384103,
"acc_stderr,none": 0.008797147564007037,
"alias": " - STEM"
},
"openaimmlu_abstract_algebra": {
"alias": " - abstract_algebra",
"acc,none": 0.42,
"acc_stderr,none": 0.049604496374885836
},
"openaimmlu_astronomy": {
"alias": " - astronomy",
"acc,none": 0.5394736842105263,
"acc_stderr,none": 0.04056242252249034
},
"openaimmlu_college_biology": {
"alias": " - college_biology",
"acc,none": 0.5069444444444444,
"acc_stderr,none": 0.04180806750294938
},
"openaimmlu_college_chemistry": {
"alias": " - college_chemistry",
"acc,none": 0.38,
"acc_stderr,none": 0.048783173121456316
},
"openaimmlu_college_computer_science": {
"alias": " - college_computer_science",
"acc,none": 0.34,
"acc_stderr,none": 0.04760952285695235
},
"openaimmlu_college_mathematics": {
"alias": " - college_mathematics",
"acc,none": 0.27,
"acc_stderr,none": 0.044619604333847394
},
"openaimmlu_college_physics": {
"alias": " - college_physics",
"acc,none": 0.23529411764705882,
"acc_stderr,none": 0.042207736591714534
},
"openaimmlu_computer_security": {
"alias": " - computer_security",
"acc,none": 0.6,
"acc_stderr,none": 0.04923659639173309
},
"openaimmlu_conceptual_physics": {
"alias": " - conceptual_physics",
"acc,none": 0.44680851063829785,
"acc_stderr,none": 0.0325005368436584
},
"openaimmlu_econometrics": {
"alias": " - econometrics",
"acc,none": 0.35964912280701755,
"acc_stderr,none": 0.04514496132873633
},
"openaimmlu_electrical_engineering": {
"alias": " - electrical_engineering",
"acc,none": 0.4482758620689655,
"acc_stderr,none": 0.04144311810878151
},
"openaimmlu_elementary_mathematics": {
"alias": " - elementary_mathematics",
"acc,none": 0.3544973544973545,
"acc_stderr,none": 0.024636830602842
},
"openaimmlu_high_school_biology": {
"alias": " - high_school_biology",
"acc,none": 0.5774193548387097,
"acc_stderr,none": 0.02810096472427264
},
"openaimmlu_high_school_chemistry": {
"alias": " - high_school_chemistry",
"acc,none": 0.3891625615763547,
"acc_stderr,none": 0.03430462416103872
},
"openaimmlu_high_school_computer_science": {
"alias": " - high_school_computer_science",
"acc,none": 0.59,
"acc_stderr,none": 0.04943110704237101
},
"openaimmlu_high_school_mathematics": {
"alias": " - high_school_mathematics",
"acc,none": 0.3296296296296296,
"acc_stderr,none": 0.02866120111652458
},
"openaimmlu_high_school_physics": {
"alias": " - high_school_physics",
"acc,none": 0.3509933774834437,
"acc_stderr,none": 0.03896981964257375
},
"openaimmlu_high_school_statistics": {
"alias": " - high_school_statistics",
"acc,none": 0.3148148148148148,
"acc_stderr,none": 0.03167468706828979
},
"openaimmlu_humanities": {
"acc,none": 0.6058758314855875,
"acc_stderr,none": 0.011278032493102804,
"alias": " - Humanities"
},
"openaimmlu_high_school_european_history": {
"alias": " - high_school_european_history",
"acc,none": 0.7393939393939394,
"acc_stderr,none": 0.03427743175816524
},
"openaimmlu_high_school_us_history": {
"alias": " - high_school_us_history",
"acc,none": 0.6911764705882353,
"acc_stderr,none": 0.03242661719827218
},
"openaimmlu_high_school_world_history": {
"alias": " - high_school_world_history",
"acc,none": 0.7341772151898734,
"acc_stderr,none": 0.028756799629658332
},
"openaimmlu_international_law": {
"alias": " - international_law",
"acc,none": 0.6776859504132231,
"acc_stderr,none": 0.042664163633521685
},
"openaimmlu_jurisprudence": {
"alias": " - jurisprudence",
"acc,none": 0.6388888888888888,
"acc_stderr,none": 0.04643454608906275
},
"openaimmlu_logical_fallacies": {
"alias": " - logical_fallacies",
"acc,none": 0.5766871165644172,
"acc_stderr,none": 0.03881891213334384
},
"openaimmlu_philosophy": {
"alias": " - philosophy",
"acc,none": 0.5112540192926045,
"acc_stderr,none": 0.028390897396863533
},
"openaimmlu_prehistory": {
"alias": " - prehistory",
"acc,none": 0.45987654320987653,
"acc_stderr,none": 0.02773102275353927
},
"openaimmlu_world_religions": {
"alias": " - world_religions",
"acc,none": 0.6023391812865497,
"acc_stderr,none": 0.03753638955761691
},
"openaimmlu_other": {
"acc,none": 0.49730276466621715,
"acc_stderr,none": 0.006341766264221109,
"alias": " - Other"
},
"openaimmlu_anatomy": {
"alias": " - anatomy",
"acc,none": 0.45925925925925926,
"acc_stderr,none": 0.04304979692464243
},
"openaimmlu_clinical_knowledge": {
"alias": " - clinical_knowledge",
"acc,none": 0.5471698113207547,
"acc_stderr,none": 0.030635627957961816
},
"openaimmlu_college_medicine": {
"alias": " - college_medicine",
"acc,none": 0.4624277456647399,
"acc_stderr,none": 0.0380168510452446
},
"openaimmlu_formal_logic": {
"alias": " - formal_logic",
"acc,none": 0.4126984126984127,
"acc_stderr,none": 0.04403438954768177
},
"openaimmlu_global_facts": {
"alias": " - global_facts",
"acc,none": 0.37,
"acc_stderr,none": 0.048523658709390974
},
"openaimmlu_high_school_geography": {
"alias": " - high_school_geography",
"acc,none": 0.696969696969697,
"acc_stderr,none": 0.032742879140268674
},
"openaimmlu_high_school_psychology": {
"alias": " - high_school_psychology",
"acc,none": 0.655045871559633,
"acc_stderr,none": 0.020380605405066966
},
"openaimmlu_human_aging": {
"alias": " - human_aging",
"acc,none": 0.5650224215246636,
"acc_stderr,none": 0.033272833702713445
},
"openaimmlu_machine_learning": {
"alias": " - machine_learning",
"acc,none": 0.33035714285714285,
"acc_stderr,none": 0.04464285714285714
},
"openaimmlu_medical_genetics": {
"alias": " - medical_genetics",
"acc,none": 0.48,
"acc_stderr,none": 0.050211673156867795
},
"openaimmlu_miscellaneous": {
"alias": " - miscellaneous",
"acc,none": 0.6475095785440613,
"acc_stderr,none": 0.017084150244081376
},
"openaimmlu_nutrition": {
"alias": " - nutrition",
"acc,none": 0.565359477124183,
"acc_stderr,none": 0.028384256704883037
},
"openaimmlu_professional_accounting": {
"alias": " - professional_accounting",
"acc,none": 0.3723404255319149,
"acc_stderr,none": 0.02883892147125145
},
"openaimmlu_professional_law": {
"alias": " - professional_law",
"acc,none": 0.39048239895697523,
"acc_stderr,none": 0.012460135913945071
},
"openaimmlu_professional_medicine": {
"alias": " - professional_medicine",
"acc,none": 0.4375,
"acc_stderr,none": 0.030134614954403924
},
"openaimmlu_professional_psychology": {
"alias": " - professional_psychology",
"acc,none": 0.46895424836601307,
"acc_stderr,none": 0.02018880445636189
},
"openaimmlu_virology": {
"alias": " - virology",
"acc,none": 0.46987951807228917,
"acc_stderr,none": 0.03885425420866766
},
"openaimmlu_social_science": {
"acc,none": 0.5249543517954961,
"acc_stderr,none": 0.008306273559742111,
"alias": " - Social Science"
},
"openaimmlu_business_ethics": {
"alias": " - business_ethics",
"acc,none": 0.64,
"acc_stderr,none": 0.048241815132442176
},
"openaimmlu_high_school_government_and_politics": {
"alias": " - high_school_government_and_politics",
"acc,none": 0.6528497409326425,
"acc_stderr,none": 0.03435696168361355
},
"openaimmlu_high_school_macroeconomics": {
"alias": " - high_school_macroeconomics",
"acc,none": 0.5102564102564102,
"acc_stderr,none": 0.025345672221942374
},
"openaimmlu_high_school_microeconomics": {
"alias": " - high_school_microeconomics",
"acc,none": 0.5042016806722689,
"acc_stderr,none": 0.03247734334448111
},
"openaimmlu_human_sexuality": {
"alias": " - human_sexuality",
"acc,none": 0.6183206106870229,
"acc_stderr,none": 0.04260735157644561
},
"openaimmlu_management": {
"alias": " - management",
"acc,none": 0.6310679611650486,
"acc_stderr,none": 0.0477761518115674
},
"openaimmlu_marketing": {
"alias": " - marketing",
"acc,none": 0.7350427350427351,
"acc_stderr,none": 0.02891120880274948
},
"openaimmlu_moral_disputes": {
"alias": " - moral_disputes",
"acc,none": 0.5520231213872833,
"acc_stderr,none": 0.026772990653361833
},
"openaimmlu_moral_scenarios": {
"alias": " - moral_scenarios",
"acc,none": 0.3005586592178771,
"acc_stderr,none": 0.01533456680625117
},
"openaimmlu_public_relations": {
"alias": " - public_relations",
"acc,none": 0.6454545454545455,
"acc_stderr,none": 0.04582004841505417
},
"openaimmlu_security_studies": {
"alias": " - security_studies",
"acc,none": 0.6244897959183674,
"acc_stderr,none": 0.03100120903989484
},
"openaimmlu_sociology": {
"alias": " - sociology",
"acc,none": 0.6865671641791045,
"acc_stderr,none": 0.032801882053486435
},
"openaimmlu_us_foreign_policy": {
"alias": " - us_foreign_policy",
"acc,none": 0.76,
"acc_stderr,none": 0.04292346959909282
}
},
"groups": {
"openaimmlu": {
"acc,none": 0.49992878507335137,
"acc_stderr,none": 0.004078575700822945,
"alias": "openaimmlu"
},
"openaimmlu_STEM": {
"acc,none": 0.41456953642384103,
"acc_stderr,none": 0.008797147564007037,
"alias": " - STEM"
},
"openaimmlu_humanities": {
"acc,none": 0.6058758314855875,
"acc_stderr,none": 0.011278032493102804,
"alias": " - Humanities"
},
"openaimmlu_other": {
"acc,none": 0.49730276466621715,
"acc_stderr,none": 0.006341766264221109,
"alias": " - Other"
},
"openaimmlu_social_science": {
"acc,none": 0.5249543517954961,
"acc_stderr,none": 0.008306273559742111,
"alias": " - Social Science"
}
},
"group_subtasks": {
"openaimmlu_humanities": [
"openaimmlu_philosophy",
"openaimmlu_world_religions",
"openaimmlu_high_school_us_history",
"openaimmlu_prehistory",
"openaimmlu_jurisprudence",
"openaimmlu_high_school_world_history",
"openaimmlu_logical_fallacies",
"openaimmlu_high_school_european_history",
"openaimmlu_international_law"
],
"openaimmlu_social_science": [
"openaimmlu_management",
"openaimmlu_moral_disputes",
"openaimmlu_moral_scenarios",
"openaimmlu_us_foreign_policy",
"openaimmlu_high_school_macroeconomics",
"openaimmlu_public_relations",
"openaimmlu_security_studies",
"openaimmlu_human_sexuality",
"openaimmlu_sociology",
"openaimmlu_high_school_microeconomics",
"openaimmlu_high_school_government_and_politics",
"openaimmlu_marketing",
"openaimmlu_business_ethics"
],
"openaimmlu_other": [
"openaimmlu_medical_genetics",
"openaimmlu_anatomy",
"openaimmlu_virology",
"openaimmlu_global_facts",
"openaimmlu_nutrition",
"openaimmlu_high_school_geography",
"openaimmlu_college_medicine",
"openaimmlu_professional_accounting",
"openaimmlu_machine_learning",
"openaimmlu_professional_psychology",
"openaimmlu_miscellaneous",
"openaimmlu_clinical_knowledge",
"openaimmlu_professional_medicine",
"openaimmlu_human_aging",
"openaimmlu_formal_logic",
"openaimmlu_high_school_psychology",
"openaimmlu_professional_law"
],
"openaimmlu_STEM": [
"openaimmlu_college_physics",
"openaimmlu_college_chemistry",
"openaimmlu_elementary_mathematics",
"openaimmlu_astronomy",
"openaimmlu_high_school_computer_science",
"openaimmlu_college_mathematics",
"openaimmlu_econometrics",
"openaimmlu_high_school_chemistry",
"openaimmlu_college_biology",
"openaimmlu_high_school_biology",
"openaimmlu_abstract_algebra",
"openaimmlu_computer_security",
"openaimmlu_high_school_physics",
"openaimmlu_high_school_statistics",
"openaimmlu_electrical_engineering",
"openaimmlu_college_computer_science",
"openaimmlu_conceptual_physics",
"openaimmlu_high_school_mathematics"
],
"openaimmlu": [
"openaimmlu_STEM",
"openaimmlu_other",
"openaimmlu_social_science",
"openaimmlu_humanities"
]
},
"configs": {
"openaimmlu_abstract_algebra": {
"task": "openaimmlu_abstract_algebra",
"task_alias": "abstract_algebra",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "abstract_algebra",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_anatomy": {
"task": "openaimmlu_anatomy",
"task_alias": "anatomy",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "anatomy",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_astronomy": {
"task": "openaimmlu_astronomy",
"task_alias": "astronomy",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "astronomy",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_business_ethics": {
"task": "openaimmlu_business_ethics",
"task_alias": "business_ethics",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "business_ethics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_clinical_knowledge": {
"task": "openaimmlu_clinical_knowledge",
"task_alias": "clinical_knowledge",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "clinical_knowledge",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_college_biology": {
"task": "openaimmlu_college_biology",
"task_alias": "college_biology",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "college_biology",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_college_chemistry": {
"task": "openaimmlu_college_chemistry",
"task_alias": "college_chemistry",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "college_chemistry",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_college_computer_science": {
"task": "openaimmlu_college_computer_science",
"task_alias": "college_computer_science",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "college_computer_science",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_college_mathematics": {
"task": "openaimmlu_college_mathematics",
"task_alias": "college_mathematics",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "college_mathematics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_college_medicine": {
"task": "openaimmlu_college_medicine",
"task_alias": "college_medicine",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "college_medicine",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_college_physics": {
"task": "openaimmlu_college_physics",
"task_alias": "college_physics",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "college_physics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_computer_security": {
"task": "openaimmlu_computer_security",
"task_alias": "computer_security",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "computer_security",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_conceptual_physics": {
"task": "openaimmlu_conceptual_physics",
"task_alias": "conceptual_physics",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "conceptual_physics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_econometrics": {
"task": "openaimmlu_econometrics",
"task_alias": "econometrics",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "econometrics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_electrical_engineering": {
"task": "openaimmlu_electrical_engineering",
"task_alias": "electrical_engineering",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "electrical_engineering",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_elementary_mathematics": {
"task": "openaimmlu_elementary_mathematics",
"task_alias": "elementary_mathematics",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "elementary_mathematics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_formal_logic": {
"task": "openaimmlu_formal_logic",
"task_alias": "formal_logic",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "formal_logic",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_global_facts": {
"task": "openaimmlu_global_facts",
"task_alias": "global_facts",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "global_facts",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_biology": {
"task": "openaimmlu_high_school_biology",
"task_alias": "high_school_biology",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_biology",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_chemistry": {
"task": "openaimmlu_high_school_chemistry",
"task_alias": "high_school_chemistry",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_chemistry",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_computer_science": {
"task": "openaimmlu_high_school_computer_science",
"task_alias": "high_school_computer_science",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_computer_science",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_european_history": {
"task": "openaimmlu_high_school_european_history",
"task_alias": "high_school_european_history",
"tag": "openaimmlu_humanities_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_european_history",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_geography": {
"task": "openaimmlu_high_school_geography",
"task_alias": "high_school_geography",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_geography",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_government_and_politics": {
"task": "openaimmlu_high_school_government_and_politics",
"task_alias": "high_school_government_and_politics",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_government_and_politics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_macroeconomics": {
"task": "openaimmlu_high_school_macroeconomics",
"task_alias": "high_school_macroeconomics",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_macroeconomics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_mathematics": {
"task": "openaimmlu_high_school_mathematics",
"task_alias": "high_school_mathematics",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_mathematics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_microeconomics": {
"task": "openaimmlu_high_school_microeconomics",
"task_alias": "high_school_microeconomics",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_microeconomics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_physics": {
"task": "openaimmlu_high_school_physics",
"task_alias": "high_school_physics",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_physics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_psychology": {
"task": "openaimmlu_high_school_psychology",
"task_alias": "high_school_psychology",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_psychology",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_statistics": {
"task": "openaimmlu_high_school_statistics",
"task_alias": "high_school_statistics",
"tag": "openaimmlu_STEM_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_statistics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_us_history": {
"task": "openaimmlu_high_school_us_history",
"task_alias": "high_school_us_history",
"tag": "openaimmlu_humanities_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_us_history",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_high_school_world_history": {
"task": "openaimmlu_high_school_world_history",
"task_alias": "high_school_world_history",
"tag": "openaimmlu_humanities_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "high_school_world_history",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_human_aging": {
"task": "openaimmlu_human_aging",
"task_alias": "human_aging",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "human_aging",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_human_sexuality": {
"task": "openaimmlu_human_sexuality",
"task_alias": "human_sexuality",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "human_sexuality",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_international_law": {
"task": "openaimmlu_international_law",
"task_alias": "international_law",
"tag": "openaimmlu_humanities_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "international_law",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_jurisprudence": {
"task": "openaimmlu_jurisprudence",
"task_alias": "jurisprudence",
"tag": "openaimmlu_humanities_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "jurisprudence",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_logical_fallacies": {
"task": "openaimmlu_logical_fallacies",
"task_alias": "logical_fallacies",
"tag": "openaimmlu_humanities_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "logical_fallacies",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_machine_learning": {
"task": "openaimmlu_machine_learning",
"task_alias": "machine_learning",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "machine_learning",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_management": {
"task": "openaimmlu_management",
"task_alias": "management",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "management",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_marketing": {
"task": "openaimmlu_marketing",
"task_alias": "marketing",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "marketing",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_medical_genetics": {
"task": "openaimmlu_medical_genetics",
"task_alias": "medical_genetics",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "medical_genetics",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_miscellaneous": {
"task": "openaimmlu_miscellaneous",
"task_alias": "miscellaneous",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "miscellaneous",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_moral_disputes": {
"task": "openaimmlu_moral_disputes",
"task_alias": "moral_disputes",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "moral_disputes",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_moral_scenarios": {
"task": "openaimmlu_moral_scenarios",
"task_alias": "moral_scenarios",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "moral_scenarios",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_nutrition": {
"task": "openaimmlu_nutrition",
"task_alias": "nutrition",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "nutrition",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_philosophy": {
"task": "openaimmlu_philosophy",
"task_alias": "philosophy",
"tag": "openaimmlu_humanities_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "philosophy",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_prehistory": {
"task": "openaimmlu_prehistory",
"task_alias": "prehistory",
"tag": "openaimmlu_humanities_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "prehistory",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_professional_accounting": {
"task": "openaimmlu_professional_accounting",
"task_alias": "professional_accounting",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "professional_accounting",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_professional_law": {
"task": "openaimmlu_professional_law",
"task_alias": "professional_law",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "professional_law",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_professional_medicine": {
"task": "openaimmlu_professional_medicine",
"task_alias": "professional_medicine",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "professional_medicine",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_professional_psychology": {
"task": "openaimmlu_professional_psychology",
"task_alias": "professional_psychology",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "professional_psychology",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_public_relations": {
"task": "openaimmlu_public_relations",
"task_alias": "public_relations",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "public_relations",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_security_studies": {
"task": "openaimmlu_security_studies",
"task_alias": "security_studies",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "security_studies",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_sociology": {
"task": "openaimmlu_sociology",
"task_alias": "sociology",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "sociology",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_us_foreign_policy": {
"task": "openaimmlu_us_foreign_policy",
"task_alias": "us_foreign_policy",
"tag": "openaimmlu_social_science_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "us_foreign_policy",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_virology": {
"task": "openaimmlu_virology",
"task_alias": "virology",
"tag": "openaimmlu_other_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "virology",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"openaimmlu_world_religions": {
"task": "openaimmlu_world_religions",
"task_alias": "world_religions",
"tag": "openaimmlu_humanities_tasks",
"dataset_path": "khalidalt/openai_mmlu_arabic",
"dataset_name": "world_religions",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
}
},
"versions": {
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"openaimmlu_STEM": 0,
"openaimmlu_abstract_algebra": 0.0,
"openaimmlu_anatomy": 0.0,
"openaimmlu_astronomy": 0.0,
"openaimmlu_business_ethics": 0.0,
"openaimmlu_clinical_knowledge": 0.0,
"openaimmlu_college_biology": 0.0,
"openaimmlu_college_chemistry": 0.0,
"openaimmlu_college_computer_science": 0.0,
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"openaimmlu_college_medicine": 0.0,
"openaimmlu_college_physics": 0.0,
"openaimmlu_computer_security": 0.0,
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"openaimmlu_electrical_engineering": 0.0,
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},
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},
"higher_is_better": {
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"acc": true
},
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"acc": true
},
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},
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},
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},
"openaimmlu_business_ethics": {
"acc": true
},
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},
"openaimmlu_college_biology": {
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},
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},
"openaimmlu_college_computer_science": {
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},
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},
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"acc": true
},
"openaimmlu_college_physics": {
"acc": true
},
"openaimmlu_computer_security": {
"acc": true
},
"openaimmlu_conceptual_physics": {
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},
"openaimmlu_econometrics": {
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},
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},
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},
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"acc": true
},
"openaimmlu_global_facts": {
"acc": true
},
"openaimmlu_high_school_biology": {
"acc": true
},
"openaimmlu_high_school_chemistry": {
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},
"openaimmlu_high_school_computer_science": {
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},
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},
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},
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},
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},
"openaimmlu_high_school_mathematics": {
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},
"openaimmlu_high_school_microeconomics": {
"acc": true
},
"openaimmlu_high_school_physics": {
"acc": true
},
"openaimmlu_high_school_psychology": {
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},
"openaimmlu_high_school_statistics": {
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},
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},
"openaimmlu_high_school_world_history": {
"acc": true
},
"openaimmlu_human_aging": {
"acc": true
},
"openaimmlu_human_sexuality": {
"acc": true
},
"openaimmlu_humanities": {
"acc": true
},
"openaimmlu_international_law": {
"acc": true
},
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"acc": true
},
"openaimmlu_logical_fallacies": {
"acc": true
},
"openaimmlu_machine_learning": {
"acc": true
},
"openaimmlu_management": {
"acc": true
},
"openaimmlu_marketing": {
"acc": true
},
"openaimmlu_medical_genetics": {
"acc": true
},
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"acc": true
},
"openaimmlu_moral_disputes": {
"acc": true
},
"openaimmlu_moral_scenarios": {
"acc": true
},
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"acc": true
},
"openaimmlu_other": {
"acc": true
},
"openaimmlu_philosophy": {
"acc": true
},
"openaimmlu_prehistory": {
"acc": true
},
"openaimmlu_professional_accounting": {
"acc": true
},
"openaimmlu_professional_law": {
"acc": true
},
"openaimmlu_professional_medicine": {
"acc": true
},
"openaimmlu_professional_psychology": {
"acc": true
},
"openaimmlu_public_relations": {
"acc": true
},
"openaimmlu_security_studies": {
"acc": true
},
"openaimmlu_social_science": {
"acc": true
},
"openaimmlu_sociology": {
"acc": true
},
"openaimmlu_us_foreign_policy": {
"acc": true
},
"openaimmlu_virology": {
"acc": true
},
"openaimmlu_world_religions": {
"acc": true
}
},
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"effective": 102
},
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"original": 100,
"effective": 100
},
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"original": 378,
"effective": 378
},
"openaimmlu_astronomy": {
"original": 152,
"effective": 152
},
"openaimmlu_high_school_computer_science": {
"original": 100,
"effective": 100
},
"openaimmlu_college_mathematics": {
"original": 100,
"effective": 100
},
"openaimmlu_econometrics": {
"original": 114,
"effective": 114
},
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"effective": 203
},
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"effective": 144
},
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"openaimmlu_professional_law": {
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"openaimmlu_moral_disputes": {
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"openaimmlu_moral_scenarios": {
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"openaimmlu_high_school_government_and_politics": {
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},
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"openaimmlu_world_religions": {
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},
"openaimmlu_high_school_us_history": {
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"openaimmlu_high_school_world_history": {
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"openaimmlu_logical_fallacies": {
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},
"config": {
"model": "hf",
"model_args": "pretrained=FreedomIntelligence/AceGPT-v2-8B-Chat,trust_remote_code=True,cache_dir=/tmp,parallelize=False",
"model_num_parameters": 8030261248,
"model_dtype": "torch.float16",
"model_revision": "main",
"model_sha": "562d0998c03c02d315e346f81650a43955711901",
"batch_size": "auto",
"batch_sizes": [
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"limit": null,
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"gen_kwargs": null,
"random_seed": 0,
"numpy_seed": 1234,
"torch_seed": 1234,
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},
"git_hash": "5e10e017",
"date": 1736967434.1317873,
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"tokenizer_eos_token": [
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],
"tokenizer_bos_token": [
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],
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"max_length": 8192,
"task_hashes": {},
"model_source": "hf",
"model_name": "FreedomIntelligence/AceGPT-v2-8B-Chat",
"model_name_sanitized": "FreedomIntelligence__AceGPT-v2-8B-Chat",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 3051.767455257,
"end_time": 3330.634011851,
"total_evaluation_time_seconds": "278.86655659400003"
}
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