diff --git "a/evaluations/ar/AceGPT-v2-8B-Chat/openaimmlu_0_shot.json" "b/evaluations/ar/AceGPT-v2-8B-Chat/openaimmlu_0_shot.json" new file mode 100644--- /dev/null +++ "b/evaluations/ar/AceGPT-v2-8B-Chat/openaimmlu_0_shot.json" @@ -0,0 +1,2662 @@ +{ + "results": { + "openaimmlu": { + "acc,none": 0.49992878507335137, + "acc_stderr,none": 0.004078575700822945, + "alias": "openaimmlu" + }, + "openaimmlu_STEM": { + "acc,none": 0.41456953642384103, + "acc_stderr,none": 0.008797147564007037, + "alias": " - STEM" + }, + "openaimmlu_abstract_algebra": { + "alias": " - abstract_algebra", + "acc,none": 0.42, + "acc_stderr,none": 0.049604496374885836 + }, + "openaimmlu_astronomy": { + "alias": " - astronomy", + "acc,none": 0.5394736842105263, + "acc_stderr,none": 0.04056242252249034 + }, + "openaimmlu_college_biology": { + "alias": " - college_biology", + "acc,none": 0.5069444444444444, + "acc_stderr,none": 0.04180806750294938 + }, + "openaimmlu_college_chemistry": { + "alias": " - college_chemistry", + "acc,none": 0.38, + "acc_stderr,none": 0.048783173121456316 + }, + "openaimmlu_college_computer_science": { + "alias": " - college_computer_science", + "acc,none": 0.34, + "acc_stderr,none": 0.04760952285695235 + }, + "openaimmlu_college_mathematics": { + "alias": " - college_mathematics", + "acc,none": 0.27, + "acc_stderr,none": 0.044619604333847394 + }, + "openaimmlu_college_physics": { + "alias": " - college_physics", + "acc,none": 0.23529411764705882, + "acc_stderr,none": 0.042207736591714534 + }, + "openaimmlu_computer_security": { + "alias": " - computer_security", + "acc,none": 0.6, + "acc_stderr,none": 0.04923659639173309 + }, + "openaimmlu_conceptual_physics": { + "alias": " - conceptual_physics", + "acc,none": 0.44680851063829785, + "acc_stderr,none": 0.0325005368436584 + }, + "openaimmlu_econometrics": { + "alias": " - econometrics", + "acc,none": 0.35964912280701755, + "acc_stderr,none": 0.04514496132873633 + }, + "openaimmlu_electrical_engineering": { + "alias": " - electrical_engineering", + "acc,none": 0.4482758620689655, + "acc_stderr,none": 0.04144311810878151 + }, + "openaimmlu_elementary_mathematics": { + "alias": " - elementary_mathematics", + "acc,none": 0.3544973544973545, + "acc_stderr,none": 0.024636830602842 + }, + "openaimmlu_high_school_biology": { + "alias": " - high_school_biology", + "acc,none": 0.5774193548387097, + "acc_stderr,none": 0.02810096472427264 + }, + "openaimmlu_high_school_chemistry": { + "alias": " - high_school_chemistry", + "acc,none": 0.3891625615763547, + "acc_stderr,none": 0.03430462416103872 + }, + "openaimmlu_high_school_computer_science": { + "alias": " - high_school_computer_science", + "acc,none": 0.59, + "acc_stderr,none": 0.04943110704237101 + }, + "openaimmlu_high_school_mathematics": { + "alias": " - high_school_mathematics", + "acc,none": 0.3296296296296296, + "acc_stderr,none": 0.02866120111652458 + }, + "openaimmlu_high_school_physics": { + "alias": " - high_school_physics", + "acc,none": 0.3509933774834437, + "acc_stderr,none": 0.03896981964257375 + }, + "openaimmlu_high_school_statistics": { + "alias": " - high_school_statistics", + "acc,none": 0.3148148148148148, + "acc_stderr,none": 0.03167468706828979 + }, + "openaimmlu_humanities": { + "acc,none": 0.6058758314855875, + "acc_stderr,none": 0.011278032493102804, + "alias": " - Humanities" + }, + "openaimmlu_high_school_european_history": { + "alias": " - high_school_european_history", + "acc,none": 0.7393939393939394, + "acc_stderr,none": 0.03427743175816524 + }, + "openaimmlu_high_school_us_history": { + "alias": " - high_school_us_history", + "acc,none": 0.6911764705882353, + "acc_stderr,none": 0.03242661719827218 + }, + "openaimmlu_high_school_world_history": { + "alias": " - high_school_world_history", + "acc,none": 0.7341772151898734, + "acc_stderr,none": 0.028756799629658332 + }, + "openaimmlu_international_law": { + "alias": " - international_law", + "acc,none": 0.6776859504132231, + "acc_stderr,none": 0.042664163633521685 + }, + "openaimmlu_jurisprudence": { + "alias": " - jurisprudence", + "acc,none": 0.6388888888888888, + "acc_stderr,none": 0.04643454608906275 + }, + "openaimmlu_logical_fallacies": { + "alias": " - logical_fallacies", + "acc,none": 0.5766871165644172, + "acc_stderr,none": 0.03881891213334384 + }, + "openaimmlu_philosophy": { + "alias": " - philosophy", + "acc,none": 0.5112540192926045, + "acc_stderr,none": 0.028390897396863533 + }, + "openaimmlu_prehistory": { + "alias": " - prehistory", + "acc,none": 0.45987654320987653, + "acc_stderr,none": 0.02773102275353927 + }, + "openaimmlu_world_religions": { + "alias": " - world_religions", + "acc,none": 0.6023391812865497, + "acc_stderr,none": 0.03753638955761691 + }, + "openaimmlu_other": { + "acc,none": 0.49730276466621715, + "acc_stderr,none": 0.006341766264221109, + "alias": " - Other" + }, + "openaimmlu_anatomy": { + "alias": " - anatomy", + "acc,none": 0.45925925925925926, + "acc_stderr,none": 0.04304979692464243 + }, + "openaimmlu_clinical_knowledge": { + "alias": " - clinical_knowledge", + "acc,none": 0.5471698113207547, + "acc_stderr,none": 0.030635627957961816 + }, + "openaimmlu_college_medicine": { + "alias": " - college_medicine", + "acc,none": 0.4624277456647399, + "acc_stderr,none": 0.0380168510452446 + }, + "openaimmlu_formal_logic": { + "alias": " - formal_logic", + "acc,none": 0.4126984126984127, + "acc_stderr,none": 0.04403438954768177 + }, + "openaimmlu_global_facts": { + "alias": " - global_facts", + "acc,none": 0.37, + "acc_stderr,none": 0.048523658709390974 + }, + "openaimmlu_high_school_geography": { + "alias": " - high_school_geography", + "acc,none": 0.696969696969697, + "acc_stderr,none": 0.032742879140268674 + }, + "openaimmlu_high_school_psychology": { + "alias": " - high_school_psychology", + "acc,none": 0.655045871559633, + "acc_stderr,none": 0.020380605405066966 + }, + "openaimmlu_human_aging": { + "alias": " - human_aging", + "acc,none": 0.5650224215246636, + "acc_stderr,none": 0.033272833702713445 + }, + "openaimmlu_machine_learning": { + "alias": " - machine_learning", + "acc,none": 0.33035714285714285, + "acc_stderr,none": 0.04464285714285714 + }, + "openaimmlu_medical_genetics": { + "alias": " - medical_genetics", + "acc,none": 0.48, + "acc_stderr,none": 0.050211673156867795 + }, + "openaimmlu_miscellaneous": { + "alias": " - miscellaneous", + "acc,none": 0.6475095785440613, + "acc_stderr,none": 0.017084150244081376 + }, + "openaimmlu_nutrition": { + "alias": " - nutrition", + "acc,none": 0.565359477124183, + "acc_stderr,none": 0.028384256704883037 + }, + "openaimmlu_professional_accounting": { + "alias": " - professional_accounting", + "acc,none": 0.3723404255319149, + "acc_stderr,none": 0.02883892147125145 + }, + "openaimmlu_professional_law": { + "alias": " - professional_law", + "acc,none": 0.39048239895697523, + "acc_stderr,none": 0.012460135913945071 + }, + "openaimmlu_professional_medicine": { + "alias": " - professional_medicine", + "acc,none": 0.4375, + "acc_stderr,none": 0.030134614954403924 + }, + "openaimmlu_professional_psychology": { + "alias": " - professional_psychology", + "acc,none": 0.46895424836601307, + "acc_stderr,none": 0.02018880445636189 + }, + "openaimmlu_virology": { + "alias": " - virology", + "acc,none": 0.46987951807228917, + "acc_stderr,none": 0.03885425420866766 + }, + "openaimmlu_social_science": { + "acc,none": 0.5249543517954961, + "acc_stderr,none": 0.008306273559742111, + "alias": " - Social Science" + }, + "openaimmlu_business_ethics": { + "alias": " - business_ethics", + "acc,none": 0.64, + "acc_stderr,none": 0.048241815132442176 + }, + "openaimmlu_high_school_government_and_politics": { + "alias": " - high_school_government_and_politics", + "acc,none": 0.6528497409326425, + "acc_stderr,none": 0.03435696168361355 + }, + "openaimmlu_high_school_macroeconomics": { + "alias": " - high_school_macroeconomics", + "acc,none": 0.5102564102564102, + "acc_stderr,none": 0.025345672221942374 + }, + "openaimmlu_high_school_microeconomics": { + "alias": " - high_school_microeconomics", + "acc,none": 0.5042016806722689, + "acc_stderr,none": 0.03247734334448111 + }, + "openaimmlu_human_sexuality": { + "alias": " - human_sexuality", + "acc,none": 0.6183206106870229, + "acc_stderr,none": 0.04260735157644561 + }, + "openaimmlu_management": { + "alias": " - management", + "acc,none": 0.6310679611650486, + "acc_stderr,none": 0.0477761518115674 + }, + "openaimmlu_marketing": { + "alias": " - marketing", + "acc,none": 0.7350427350427351, + "acc_stderr,none": 0.02891120880274948 + }, + "openaimmlu_moral_disputes": { + "alias": " - moral_disputes", + "acc,none": 0.5520231213872833, + "acc_stderr,none": 0.026772990653361833 + }, + "openaimmlu_moral_scenarios": { + "alias": " - moral_scenarios", + "acc,none": 0.3005586592178771, + "acc_stderr,none": 0.01533456680625117 + }, + "openaimmlu_public_relations": { + "alias": " - public_relations", + "acc,none": 0.6454545454545455, + "acc_stderr,none": 0.04582004841505417 + }, + "openaimmlu_security_studies": { + "alias": " - security_studies", + "acc,none": 0.6244897959183674, + "acc_stderr,none": 0.03100120903989484 + }, + "openaimmlu_sociology": { + "alias": " - sociology", + "acc,none": 0.6865671641791045, + "acc_stderr,none": 0.032801882053486435 + }, + "openaimmlu_us_foreign_policy": { + "alias": " - us_foreign_policy", + "acc,none": 0.76, + "acc_stderr,none": 0.04292346959909282 + } + }, + "groups": { + "openaimmlu": { + "acc,none": 0.49992878507335137, + "acc_stderr,none": 0.004078575700822945, + "alias": "openaimmlu" + }, + "openaimmlu_STEM": { + "acc,none": 0.41456953642384103, + "acc_stderr,none": 0.008797147564007037, + "alias": " - STEM" + }, + "openaimmlu_humanities": { + "acc,none": 0.6058758314855875, + "acc_stderr,none": 0.011278032493102804, + "alias": " - Humanities" + }, + "openaimmlu_other": { + "acc,none": 0.49730276466621715, + "acc_stderr,none": 0.006341766264221109, + "alias": " - Other" + }, + "openaimmlu_social_science": { + "acc,none": 0.5249543517954961, + "acc_stderr,none": 0.008306273559742111, + "alias": " - Social Science" + } + }, + "group_subtasks": { + "openaimmlu_humanities": [ + "openaimmlu_philosophy", + "openaimmlu_world_religions", + "openaimmlu_high_school_us_history", + "openaimmlu_prehistory", + "openaimmlu_jurisprudence", + "openaimmlu_high_school_world_history", + "openaimmlu_logical_fallacies", + "openaimmlu_high_school_european_history", + "openaimmlu_international_law" + ], + "openaimmlu_social_science": [ + "openaimmlu_management", + "openaimmlu_moral_disputes", + "openaimmlu_moral_scenarios", + "openaimmlu_us_foreign_policy", + "openaimmlu_high_school_macroeconomics", + "openaimmlu_public_relations", + "openaimmlu_security_studies", + "openaimmlu_human_sexuality", + "openaimmlu_sociology", + "openaimmlu_high_school_microeconomics", + "openaimmlu_high_school_government_and_politics", + "openaimmlu_marketing", + "openaimmlu_business_ethics" + ], + "openaimmlu_other": [ + "openaimmlu_medical_genetics", + "openaimmlu_anatomy", + "openaimmlu_virology", + "openaimmlu_global_facts", + "openaimmlu_nutrition", + "openaimmlu_high_school_geography", + "openaimmlu_college_medicine", + "openaimmlu_professional_accounting", + "openaimmlu_machine_learning", + "openaimmlu_professional_psychology", + "openaimmlu_miscellaneous", + "openaimmlu_clinical_knowledge", + "openaimmlu_professional_medicine", + "openaimmlu_human_aging", + "openaimmlu_formal_logic", + "openaimmlu_high_school_psychology", + "openaimmlu_professional_law" + ], + "openaimmlu_STEM": [ + "openaimmlu_college_physics", + "openaimmlu_college_chemistry", + "openaimmlu_elementary_mathematics", + "openaimmlu_astronomy", + "openaimmlu_high_school_computer_science", + "openaimmlu_college_mathematics", + "openaimmlu_econometrics", + "openaimmlu_high_school_chemistry", + "openaimmlu_college_biology", + "openaimmlu_high_school_biology", + "openaimmlu_abstract_algebra", + "openaimmlu_computer_security", + "openaimmlu_high_school_physics", + "openaimmlu_high_school_statistics", + "openaimmlu_electrical_engineering", + "openaimmlu_college_computer_science", + "openaimmlu_conceptual_physics", + "openaimmlu_high_school_mathematics" + ], + "openaimmlu": [ + "openaimmlu_STEM", + "openaimmlu_other", + "openaimmlu_social_science", + "openaimmlu_humanities" + ] + }, + "configs": { + "openaimmlu_abstract_algebra": { + "task": "openaimmlu_abstract_algebra", + "task_alias": "abstract_algebra", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "abstract_algebra", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_anatomy": { + "task": "openaimmlu_anatomy", + "task_alias": "anatomy", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "anatomy", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_astronomy": { + "task": "openaimmlu_astronomy", + "task_alias": "astronomy", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "astronomy", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_business_ethics": { + "task": "openaimmlu_business_ethics", + "task_alias": "business_ethics", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "business_ethics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_clinical_knowledge": { + "task": "openaimmlu_clinical_knowledge", + "task_alias": "clinical_knowledge", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "clinical_knowledge", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_college_biology": { + "task": "openaimmlu_college_biology", + "task_alias": "college_biology", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "college_biology", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_college_chemistry": { + "task": "openaimmlu_college_chemistry", + "task_alias": "college_chemistry", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "college_chemistry", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_college_computer_science": { + "task": "openaimmlu_college_computer_science", + "task_alias": "college_computer_science", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "college_computer_science", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_college_mathematics": { + "task": "openaimmlu_college_mathematics", + "task_alias": "college_mathematics", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "college_mathematics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_college_medicine": { + "task": "openaimmlu_college_medicine", + "task_alias": "college_medicine", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "college_medicine", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_college_physics": { + "task": "openaimmlu_college_physics", + "task_alias": "college_physics", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "college_physics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_computer_security": { + "task": "openaimmlu_computer_security", + "task_alias": "computer_security", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "computer_security", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_conceptual_physics": { + "task": "openaimmlu_conceptual_physics", + "task_alias": "conceptual_physics", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "conceptual_physics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_econometrics": { + "task": "openaimmlu_econometrics", + "task_alias": "econometrics", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "econometrics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_electrical_engineering": { + "task": "openaimmlu_electrical_engineering", + "task_alias": "electrical_engineering", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "electrical_engineering", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_elementary_mathematics": { + "task": "openaimmlu_elementary_mathematics", + "task_alias": "elementary_mathematics", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "elementary_mathematics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_formal_logic": { + "task": "openaimmlu_formal_logic", + "task_alias": "formal_logic", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "formal_logic", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_global_facts": { + "task": "openaimmlu_global_facts", + "task_alias": "global_facts", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "global_facts", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_biology": { + "task": "openaimmlu_high_school_biology", + "task_alias": "high_school_biology", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_biology", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_chemistry": { + "task": "openaimmlu_high_school_chemistry", + "task_alias": "high_school_chemistry", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_chemistry", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_computer_science": { + "task": "openaimmlu_high_school_computer_science", + "task_alias": "high_school_computer_science", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_computer_science", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_european_history": { + "task": "openaimmlu_high_school_european_history", + "task_alias": "high_school_european_history", + "tag": "openaimmlu_humanities_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_european_history", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_geography": { + "task": "openaimmlu_high_school_geography", + "task_alias": "high_school_geography", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_geography", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_government_and_politics": { + "task": "openaimmlu_high_school_government_and_politics", + "task_alias": "high_school_government_and_politics", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_government_and_politics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_macroeconomics": { + "task": "openaimmlu_high_school_macroeconomics", + "task_alias": "high_school_macroeconomics", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_macroeconomics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_mathematics": { + "task": "openaimmlu_high_school_mathematics", + "task_alias": "high_school_mathematics", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_mathematics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_microeconomics": { + "task": "openaimmlu_high_school_microeconomics", + "task_alias": "high_school_microeconomics", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_microeconomics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_physics": { + "task": "openaimmlu_high_school_physics", + "task_alias": "high_school_physics", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_physics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_psychology": { + "task": "openaimmlu_high_school_psychology", + "task_alias": "high_school_psychology", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_psychology", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_statistics": { + "task": "openaimmlu_high_school_statistics", + "task_alias": "high_school_statistics", + "tag": "openaimmlu_STEM_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_statistics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_us_history": { + "task": "openaimmlu_high_school_us_history", + "task_alias": "high_school_us_history", + "tag": "openaimmlu_humanities_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_us_history", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_high_school_world_history": { + "task": "openaimmlu_high_school_world_history", + "task_alias": "high_school_world_history", + "tag": "openaimmlu_humanities_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "high_school_world_history", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_human_aging": { + "task": "openaimmlu_human_aging", + "task_alias": "human_aging", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "human_aging", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_human_sexuality": { + "task": "openaimmlu_human_sexuality", + "task_alias": "human_sexuality", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "human_sexuality", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_international_law": { + "task": "openaimmlu_international_law", + "task_alias": "international_law", + "tag": "openaimmlu_humanities_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "international_law", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_jurisprudence": { + "task": "openaimmlu_jurisprudence", + "task_alias": "jurisprudence", + "tag": "openaimmlu_humanities_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "jurisprudence", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_logical_fallacies": { + "task": "openaimmlu_logical_fallacies", + "task_alias": "logical_fallacies", + "tag": "openaimmlu_humanities_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "logical_fallacies", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_machine_learning": { + "task": "openaimmlu_machine_learning", + "task_alias": "machine_learning", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "machine_learning", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_management": { + "task": "openaimmlu_management", + "task_alias": "management", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "management", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_marketing": { + "task": "openaimmlu_marketing", + "task_alias": "marketing", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "marketing", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_medical_genetics": { + "task": "openaimmlu_medical_genetics", + "task_alias": "medical_genetics", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "medical_genetics", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_miscellaneous": { + "task": "openaimmlu_miscellaneous", + "task_alias": "miscellaneous", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "miscellaneous", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_moral_disputes": { + "task": "openaimmlu_moral_disputes", + "task_alias": "moral_disputes", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "moral_disputes", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_moral_scenarios": { + "task": "openaimmlu_moral_scenarios", + "task_alias": "moral_scenarios", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "moral_scenarios", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_nutrition": { + "task": "openaimmlu_nutrition", + "task_alias": "nutrition", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "nutrition", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_philosophy": { + "task": "openaimmlu_philosophy", + "task_alias": "philosophy", + "tag": "openaimmlu_humanities_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "philosophy", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_prehistory": { + "task": "openaimmlu_prehistory", + "task_alias": "prehistory", + "tag": "openaimmlu_humanities_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "prehistory", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_professional_accounting": { + "task": "openaimmlu_professional_accounting", + "task_alias": "professional_accounting", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "professional_accounting", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_professional_law": { + "task": "openaimmlu_professional_law", + "task_alias": "professional_law", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "professional_law", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_professional_medicine": { + "task": "openaimmlu_professional_medicine", + "task_alias": "professional_medicine", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "professional_medicine", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_professional_psychology": { + "task": "openaimmlu_professional_psychology", + "task_alias": "professional_psychology", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "professional_psychology", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_public_relations": { + "task": "openaimmlu_public_relations", + "task_alias": "public_relations", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "public_relations", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_security_studies": { + "task": "openaimmlu_security_studies", + "task_alias": "security_studies", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "security_studies", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_sociology": { + "task": "openaimmlu_sociology", + "task_alias": "sociology", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "sociology", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_us_foreign_policy": { + "task": "openaimmlu_us_foreign_policy", + "task_alias": "us_foreign_policy", + "tag": "openaimmlu_social_science_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "us_foreign_policy", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_virology": { + "task": "openaimmlu_virology", + "task_alias": "virology", + "tag": "openaimmlu_other_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "virology", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "openaimmlu_world_religions": { + "task": "openaimmlu_world_religions", + "task_alias": "world_religions", + "tag": "openaimmlu_humanities_tasks", + "dataset_path": "khalidalt/openai_mmlu_arabic", + "dataset_name": "world_religions", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", + "doc_to_text": "query", + "doc_to_target": "gold", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + } + }, + "versions": { + "openaimmlu": 0, + "openaimmlu_STEM": 0, + "openaimmlu_abstract_algebra": 0.0, + "openaimmlu_anatomy": 0.0, + "openaimmlu_astronomy": 0.0, + "openaimmlu_business_ethics": 0.0, + "openaimmlu_clinical_knowledge": 0.0, + "openaimmlu_college_biology": 0.0, + "openaimmlu_college_chemistry": 0.0, + "openaimmlu_college_computer_science": 0.0, + "openaimmlu_college_mathematics": 0.0, + "openaimmlu_college_medicine": 0.0, + "openaimmlu_college_physics": 0.0, + "openaimmlu_computer_security": 0.0, + "openaimmlu_conceptual_physics": 0.0, + "openaimmlu_econometrics": 0.0, + "openaimmlu_electrical_engineering": 0.0, + "openaimmlu_elementary_mathematics": 0.0, + "openaimmlu_formal_logic": 0.0, + "openaimmlu_global_facts": 0.0, + "openaimmlu_high_school_biology": 0.0, + "openaimmlu_high_school_chemistry": 0.0, + "openaimmlu_high_school_computer_science": 0.0, + "openaimmlu_high_school_european_history": 0.0, + "openaimmlu_high_school_geography": 0.0, + "openaimmlu_high_school_government_and_politics": 0.0, + "openaimmlu_high_school_macroeconomics": 0.0, + "openaimmlu_high_school_mathematics": 0.0, + "openaimmlu_high_school_microeconomics": 0.0, + "openaimmlu_high_school_physics": 0.0, + "openaimmlu_high_school_psychology": 0.0, + "openaimmlu_high_school_statistics": 0.0, + "openaimmlu_high_school_us_history": 0.0, + "openaimmlu_high_school_world_history": 0.0, + "openaimmlu_human_aging": 0.0, + "openaimmlu_human_sexuality": 0.0, + "openaimmlu_humanities": 0, + "openaimmlu_international_law": 0.0, + "openaimmlu_jurisprudence": 0.0, + "openaimmlu_logical_fallacies": 0.0, + "openaimmlu_machine_learning": 0.0, + "openaimmlu_management": 0.0, + "openaimmlu_marketing": 0.0, + "openaimmlu_medical_genetics": 0.0, + "openaimmlu_miscellaneous": 0.0, + "openaimmlu_moral_disputes": 0.0, + "openaimmlu_moral_scenarios": 0.0, + "openaimmlu_nutrition": 0.0, + "openaimmlu_other": 0, + "openaimmlu_philosophy": 0.0, + "openaimmlu_prehistory": 0.0, + "openaimmlu_professional_accounting": 0.0, + 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following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 48\nOn-line CPU(s) list: 0-47\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V13 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4890.88\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm 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affected\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect", + "transformers_version": "4.48.0", + "upper_git_hash": "2e5cd5395faf76fea1afc96dd0f7161a9d3aa145", + "tokenizer_pad_token": [ + "<|end_of_text|>", + "128001" + ], + "tokenizer_eos_token": [ + "<|end_of_text|>", 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