|
{ |
|
"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", |
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"doc_to_target": "gold", |
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"doc_to_choice": "choices", |
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"description": "", |
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"target_delimiter": " ", |
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"fewshot_delimiter": "\n\n", |
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{ |
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"metric": "acc", |
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"higher_is_better": true |
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} |
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], |
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"output_type": "multiple_choice", |
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"metadata": { |
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"version": 0.0 |
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} |
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}, |
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"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", |
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"doc_to_choice": "choices", |
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"description": "", |
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"target_delimiter": " ", |
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{ |
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"metric": "acc", |
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"higher_is_better": true |
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} |
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], |
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"output_type": "multiple_choice", |
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"metadata": { |
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"version": 0.0 |
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} |
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}, |
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"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", |
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"description": "", |
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"target_delimiter": " ", |
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"metric_list": [ |
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{ |
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"metric": "acc", |
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"higher_is_better": true |
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} |
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], |
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"output_type": "multiple_choice", |
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|
"should_decontaminate": false, |
|
"metadata": { |
|
"version": 0.0 |
|
} |
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}, |
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"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", |
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"description": "", |
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"target_delimiter": " ", |
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"fewshot_delimiter": "\n\n", |
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"metric_list": [ |
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{ |
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"metric": "acc", |
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"higher_is_better": true |
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} |
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], |
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"output_type": "multiple_choice", |
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|
"should_decontaminate": false, |
|
"metadata": { |
|
"version": 0.0 |
|
} |
|
}, |
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"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": " ", |
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"fewshot_delimiter": "\n\n", |
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"metric_list": [ |
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{ |
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"metric": "acc", |
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"higher_is_better": true |
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} |
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], |
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"output_type": "multiple_choice", |
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|
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|
"metadata": { |
|
"version": 0.0 |
|
} |
|
}, |
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"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", |
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"doc_to_choice": "choices", |
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"description": "", |
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{ |
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"metric": "acc", |
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} |
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], |
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"output_type": "multiple_choice", |
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"metadata": { |
|
"version": 0.0 |
|
} |
|
}, |
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"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", |
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"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", |
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"doc_to_target": "gold", |
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"doc_to_choice": "choices", |
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"description": "", |
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{ |
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"metric": "acc", |
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} |
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], |
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"output_type": "multiple_choice", |
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"version": 0.0 |
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} |
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} |
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}, |
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"acc": true |
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"acc": true |
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"acc": true |
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