| { | |
| "results": { | |
| "openaimmlu": { | |
| "acc,none": 0.3854151830223615, | |
| "acc_stderr,none": 0.004031384548470796, | |
| "alias": "openaimmlu" | |
| }, | |
| "openaimmlu_STEM": { | |
| "acc,none": 0.3258278145695364, | |
| "acc_stderr,none": 0.008457779824528174, | |
| "alias": " - STEM" | |
| }, | |
| "openaimmlu_abstract_algebra": { | |
| "alias": " - abstract_algebra", | |
| "acc,none": 0.26, | |
| "acc_stderr,none": 0.04408440022768077 | |
| }, | |
| "openaimmlu_astronomy": { | |
| "alias": " - astronomy", | |
| "acc,none": 0.4276315789473684, | |
| "acc_stderr,none": 0.04026097083296558 | |
| }, | |
| "openaimmlu_college_biology": { | |
| "alias": " - college_biology", | |
| "acc,none": 0.4097222222222222, | |
| "acc_stderr,none": 0.04112490974670787 | |
| }, | |
| "openaimmlu_college_chemistry": { | |
| "alias": " - college_chemistry", | |
| "acc,none": 0.22, | |
| "acc_stderr,none": 0.04163331998932269 | |
| }, | |
| "openaimmlu_college_computer_science": { | |
| "alias": " - college_computer_science", | |
| "acc,none": 0.36, | |
| "acc_stderr,none": 0.04824181513244218 | |
| }, | |
| "openaimmlu_college_mathematics": { | |
| "alias": " - college_mathematics", | |
| "acc,none": 0.25, | |
| "acc_stderr,none": 0.04351941398892446 | |
| }, | |
| "openaimmlu_college_physics": { | |
| "alias": " - college_physics", | |
| "acc,none": 0.22549019607843138, | |
| "acc_stderr,none": 0.041583075330832865 | |
| }, | |
| "openaimmlu_computer_security": { | |
| "alias": " - computer_security", | |
| "acc,none": 0.49, | |
| "acc_stderr,none": 0.05024183937956912 | |
| }, | |
| "openaimmlu_conceptual_physics": { | |
| "alias": " - conceptual_physics", | |
| "acc,none": 0.3191489361702128, | |
| "acc_stderr,none": 0.030472973363380045 | |
| }, | |
| "openaimmlu_econometrics": { | |
| "alias": " - econometrics", | |
| "acc,none": 0.2894736842105263, | |
| "acc_stderr,none": 0.04266339443159394 | |
| }, | |
| "openaimmlu_electrical_engineering": { | |
| "alias": " - electrical_engineering", | |
| "acc,none": 0.4, | |
| "acc_stderr,none": 0.040824829046386284 | |
| }, | |
| "openaimmlu_elementary_mathematics": { | |
| "alias": " - elementary_mathematics", | |
| "acc,none": 0.2804232804232804, | |
| "acc_stderr,none": 0.023135287974325628 | |
| }, | |
| "openaimmlu_high_school_biology": { | |
| "alias": " - high_school_biology", | |
| "acc,none": 0.3741935483870968, | |
| "acc_stderr,none": 0.027528904299845777 | |
| }, | |
| "openaimmlu_high_school_chemistry": { | |
| "alias": " - high_school_chemistry", | |
| "acc,none": 0.3694581280788177, | |
| "acc_stderr,none": 0.03395970381998575 | |
| }, | |
| "openaimmlu_high_school_computer_science": { | |
| "alias": " - high_school_computer_science", | |
| "acc,none": 0.43, | |
| "acc_stderr,none": 0.049756985195624284 | |
| }, | |
| "openaimmlu_high_school_mathematics": { | |
| "alias": " - high_school_mathematics", | |
| "acc,none": 0.3, | |
| "acc_stderr,none": 0.027940457136228402 | |
| }, | |
| "openaimmlu_high_school_physics": { | |
| "alias": " - high_school_physics", | |
| "acc,none": 0.271523178807947, | |
| "acc_stderr,none": 0.03631329803969654 | |
| }, | |
| "openaimmlu_high_school_statistics": { | |
| "alias": " - high_school_statistics", | |
| "acc,none": 0.2361111111111111, | |
| "acc_stderr,none": 0.02896370257079102 | |
| }, | |
| "openaimmlu_humanities": { | |
| "acc,none": 0.4861419068736142, | |
| "acc_stderr,none": 0.011703480584172478, | |
| "alias": " - Humanities" | |
| }, | |
| "openaimmlu_high_school_european_history": { | |
| "alias": " - high_school_european_history", | |
| "acc,none": 0.5151515151515151, | |
| "acc_stderr,none": 0.039025510073744475 | |
| }, | |
| "openaimmlu_high_school_us_history": { | |
| "alias": " - high_school_us_history", | |
| "acc,none": 0.45588235294117646, | |
| "acc_stderr,none": 0.034956245220154746 | |
| }, | |
| "openaimmlu_high_school_world_history": { | |
| "alias": " - high_school_world_history", | |
| "acc,none": 0.5991561181434599, | |
| "acc_stderr,none": 0.031900803894732356 | |
| }, | |
| "openaimmlu_international_law": { | |
| "alias": " - international_law", | |
| "acc,none": 0.5867768595041323, | |
| "acc_stderr,none": 0.04495087843548408 | |
| }, | |
| "openaimmlu_jurisprudence": { | |
| "alias": " - jurisprudence", | |
| "acc,none": 0.48148148148148145, | |
| "acc_stderr,none": 0.04830366024635331 | |
| }, | |
| "openaimmlu_logical_fallacies": { | |
| "alias": " - logical_fallacies", | |
| "acc,none": 0.43558282208588955, | |
| "acc_stderr,none": 0.03895632464138937 | |
| }, | |
| "openaimmlu_philosophy": { | |
| "alias": " - philosophy", | |
| "acc,none": 0.43729903536977494, | |
| "acc_stderr,none": 0.028173917761762878 | |
| }, | |
| "openaimmlu_prehistory": { | |
| "alias": " - prehistory", | |
| "acc,none": 0.42592592592592593, | |
| "acc_stderr,none": 0.027513747284379424 | |
| }, | |
| "openaimmlu_world_religions": { | |
| "alias": " - world_religions", | |
| "acc,none": 0.52046783625731, | |
| "acc_stderr,none": 0.038316105328219316 | |
| }, | |
| "openaimmlu_other": { | |
| "acc,none": 0.3792987188132165, | |
| "acc_stderr,none": 0.006232325281499182, | |
| "alias": " - Other" | |
| }, | |
| "openaimmlu_anatomy": { | |
| "alias": " - anatomy", | |
| "acc,none": 0.4222222222222222, | |
| "acc_stderr,none": 0.04266763404099582 | |
| }, | |
| "openaimmlu_clinical_knowledge": { | |
| "alias": " - clinical_knowledge", | |
| "acc,none": 0.3622641509433962, | |
| "acc_stderr,none": 0.0295822451283843 | |
| }, | |
| "openaimmlu_college_medicine": { | |
| "alias": " - college_medicine", | |
| "acc,none": 0.3179190751445087, | |
| "acc_stderr,none": 0.0355068398916558 | |
| }, | |
| "openaimmlu_formal_logic": { | |
| "alias": " - formal_logic", | |
| "acc,none": 0.29365079365079366, | |
| "acc_stderr,none": 0.040735243221471255 | |
| }, | |
| "openaimmlu_global_facts": { | |
| "alias": " - global_facts", | |
| "acc,none": 0.37, | |
| "acc_stderr,none": 0.04852365870939098 | |
| }, | |
| "openaimmlu_high_school_geography": { | |
| "alias": " - high_school_geography", | |
| "acc,none": 0.4797979797979798, | |
| "acc_stderr,none": 0.03559443565563919 | |
| }, | |
| "openaimmlu_high_school_psychology": { | |
| "alias": " - high_school_psychology", | |
| "acc,none": 0.42018348623853213, | |
| "acc_stderr,none": 0.021162420048273515 | |
| }, | |
| "openaimmlu_human_aging": { | |
| "alias": " - human_aging", | |
| "acc,none": 0.47085201793721976, | |
| "acc_stderr,none": 0.03350073248773404 | |
| }, | |
| "openaimmlu_machine_learning": { | |
| "alias": " - machine_learning", | |
| "acc,none": 0.375, | |
| "acc_stderr,none": 0.04595091388086298 | |
| }, | |
| "openaimmlu_medical_genetics": { | |
| "alias": " - medical_genetics", | |
| "acc,none": 0.38, | |
| "acc_stderr,none": 0.048783173121456316 | |
| }, | |
| "openaimmlu_miscellaneous": { | |
| "alias": " - miscellaneous", | |
| "acc,none": 0.5057471264367817, | |
| "acc_stderr,none": 0.017878782326129227 | |
| }, | |
| "openaimmlu_nutrition": { | |
| "alias": " - nutrition", | |
| "acc,none": 0.4542483660130719, | |
| "acc_stderr,none": 0.02850980780262657 | |
| }, | |
| "openaimmlu_professional_accounting": { | |
| "alias": " - professional_accounting", | |
| "acc,none": 0.3333333333333333, | |
| "acc_stderr,none": 0.028121636040639882 | |
| }, | |
| "openaimmlu_professional_law": { | |
| "alias": " - professional_law", | |
| "acc,none": 0.3239895697522816, | |
| "acc_stderr,none": 0.011952840809646566 | |
| }, | |
| "openaimmlu_professional_medicine": { | |
| "alias": " - professional_medicine", | |
| "acc,none": 0.22058823529411764, | |
| "acc_stderr,none": 0.025187786660227265 | |
| }, | |
| "openaimmlu_professional_psychology": { | |
| "alias": " - professional_psychology", | |
| "acc,none": 0.34967320261437906, | |
| "acc_stderr,none": 0.01929196189506638 | |
| }, | |
| "openaimmlu_virology": { | |
| "alias": " - virology", | |
| "acc,none": 0.35542168674698793, | |
| "acc_stderr,none": 0.03726214354322415 | |
| }, | |
| "openaimmlu_social_science": { | |
| "acc,none": 0.3959220937309799, | |
| "acc_stderr,none": 0.00827574379380361, | |
| "alias": " - Social Science" | |
| }, | |
| "openaimmlu_business_ethics": { | |
| "alias": " - business_ethics", | |
| "acc,none": 0.39, | |
| "acc_stderr,none": 0.04902071300001974 | |
| }, | |
| "openaimmlu_high_school_government_and_politics": { | |
| "alias": " - high_school_government_and_politics", | |
| "acc,none": 0.39378238341968913, | |
| "acc_stderr,none": 0.03526077095548237 | |
| }, | |
| "openaimmlu_high_school_macroeconomics": { | |
| "alias": " - high_school_macroeconomics", | |
| "acc,none": 0.34615384615384615, | |
| "acc_stderr,none": 0.024121125416941183 | |
| }, | |
| "openaimmlu_high_school_microeconomics": { | |
| "alias": " - high_school_microeconomics", | |
| "acc,none": 0.3445378151260504, | |
| "acc_stderr,none": 0.030868682604121633 | |
| }, | |
| "openaimmlu_human_sexuality": { | |
| "alias": " - human_sexuality", | |
| "acc,none": 0.4732824427480916, | |
| "acc_stderr,none": 0.04379024936553894 | |
| }, | |
| "openaimmlu_management": { | |
| "alias": " - management", | |
| "acc,none": 0.44660194174757284, | |
| "acc_stderr,none": 0.04922424153458933 | |
| }, | |
| "openaimmlu_marketing": { | |
| "alias": " - marketing", | |
| "acc,none": 0.5982905982905983, | |
| "acc_stderr,none": 0.03211693751051621 | |
| }, | |
| "openaimmlu_moral_disputes": { | |
| "alias": " - moral_disputes", | |
| "acc,none": 0.4797687861271676, | |
| "acc_stderr,none": 0.026897049996382875 | |
| }, | |
| "openaimmlu_moral_scenarios": { | |
| "alias": " - moral_scenarios", | |
| "acc,none": 0.24022346368715083, | |
| "acc_stderr,none": 0.014288343803925307 | |
| }, | |
| "openaimmlu_public_relations": { | |
| "alias": " - public_relations", | |
| "acc,none": 0.4, | |
| "acc_stderr,none": 0.0469237132203465 | |
| }, | |
| "openaimmlu_security_studies": { | |
| "alias": " - security_studies", | |
| "acc,none": 0.5061224489795918, | |
| "acc_stderr,none": 0.032006820201639086 | |
| }, | |
| "openaimmlu_sociology": { | |
| "alias": " - sociology", | |
| "acc,none": 0.5373134328358209, | |
| "acc_stderr,none": 0.03525675167467974 | |
| }, | |
| "openaimmlu_us_foreign_policy": { | |
| "alias": " - us_foreign_policy", | |
| "acc,none": 0.64, | |
| "acc_stderr,none": 0.04824181513244218 | |
| } | |
| }, | |
| "groups": { | |
| "openaimmlu": { | |
| "acc,none": 0.3854151830223615, | |
| "acc_stderr,none": 0.004031384548470796, | |
| "alias": "openaimmlu" | |
| }, | |
| "openaimmlu_STEM": { | |
| "acc,none": 0.3258278145695364, | |
| "acc_stderr,none": 0.008457779824528174, | |
| "alias": " - STEM" | |
| }, | |
| "openaimmlu_humanities": { | |
| "acc,none": 0.4861419068736142, | |
| "acc_stderr,none": 0.011703480584172478, | |
| "alias": " - Humanities" | |
| }, | |
| "openaimmlu_other": { | |
| "acc,none": 0.3792987188132165, | |
| "acc_stderr,none": 0.006232325281499182, | |
| "alias": " - Other" | |
| }, | |
| "openaimmlu_social_science": { | |
| "acc,none": 0.3959220937309799, | |
| "acc_stderr,none": 0.00827574379380361, | |
| "alias": " - Social Science" | |
| } | |
| }, | |
| "group_subtasks": { | |
| "openaimmlu_humanities": [ | |
| "openaimmlu_logical_fallacies", | |
| "openaimmlu_high_school_us_history", | |
| "openaimmlu_prehistory", | |
| "openaimmlu_high_school_world_history", | |
| "openaimmlu_philosophy", | |
| "openaimmlu_international_law", | |
| "openaimmlu_jurisprudence", | |
| "openaimmlu_world_religions", | |
| "openaimmlu_high_school_european_history" | |
| ], | |
| "openaimmlu_social_science": [ | |
| "openaimmlu_marketing", | |
| "openaimmlu_moral_scenarios", | |
| "openaimmlu_high_school_macroeconomics", | |
| "openaimmlu_high_school_government_and_politics", | |
| "openaimmlu_business_ethics", | |
| "openaimmlu_high_school_microeconomics", | |
| "openaimmlu_security_studies", | |
| "openaimmlu_moral_disputes", | |
| "openaimmlu_public_relations", | |
| "openaimmlu_us_foreign_policy", | |
| "openaimmlu_management", | |
| "openaimmlu_sociology", | |
| "openaimmlu_human_sexuality" | |
| ], | |
| "openaimmlu_other": [ | |
| "openaimmlu_professional_law", | |
| "openaimmlu_medical_genetics", | |
| "openaimmlu_nutrition", | |
| "openaimmlu_miscellaneous", | |
| "openaimmlu_formal_logic", | |
| "openaimmlu_high_school_geography", | |
| "openaimmlu_professional_medicine", | |
| "openaimmlu_clinical_knowledge", | |
| "openaimmlu_professional_accounting", | |
| "openaimmlu_professional_psychology", | |
| "openaimmlu_college_medicine", | |
| "openaimmlu_human_aging", | |
| "openaimmlu_high_school_psychology", | |
| "openaimmlu_anatomy", | |
| "openaimmlu_global_facts", | |
| "openaimmlu_machine_learning", | |
| "openaimmlu_virology" | |
| ], | |
| "openaimmlu_STEM": [ | |
| "openaimmlu_high_school_physics", | |
| "openaimmlu_college_biology", | |
| "openaimmlu_computer_security", | |
| "openaimmlu_electrical_engineering", | |
| "openaimmlu_college_computer_science", | |
| "openaimmlu_abstract_algebra", | |
| "openaimmlu_high_school_chemistry", | |
| "openaimmlu_high_school_biology", | |
| "openaimmlu_high_school_mathematics", | |
| "openaimmlu_high_school_statistics", | |
| "openaimmlu_elementary_mathematics", | |
| "openaimmlu_college_mathematics", | |
| "openaimmlu_college_physics", | |
| "openaimmlu_astronomy", | |
| "openaimmlu_college_chemistry", | |
| "openaimmlu_econometrics", | |
| "openaimmlu_high_school_computer_science", | |
| "openaimmlu_conceptual_physics" | |
| ], | |
| "openaimmlu": [ | |
| "openaimmlu_STEM", | |
| "openaimmlu_other", | |
| "openaimmlu_social_science", | |
| "openaimmlu_humanities" | |
| ] | |
| }, | |
| "configs": { | |
| "openaimmlu_abstract_algebra": { | |
| "task": "openaimmlu_abstract_algebra", | |
| "task_alias": "abstract_algebra", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "abstract_algebra", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_anatomy": { | |
| "task": "openaimmlu_anatomy", | |
| "task_alias": "anatomy", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "anatomy", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_astronomy": { | |
| "task": "openaimmlu_astronomy", | |
| "task_alias": "astronomy", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "astronomy", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_business_ethics": { | |
| "task": "openaimmlu_business_ethics", | |
| "task_alias": "business_ethics", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "business_ethics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_clinical_knowledge": { | |
| "task": "openaimmlu_clinical_knowledge", | |
| "task_alias": "clinical_knowledge", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "clinical_knowledge", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_college_biology": { | |
| "task": "openaimmlu_college_biology", | |
| "task_alias": "college_biology", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "college_biology", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_college_chemistry": { | |
| "task": "openaimmlu_college_chemistry", | |
| "task_alias": "college_chemistry", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "college_chemistry", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_college_computer_science": { | |
| "task": "openaimmlu_college_computer_science", | |
| "task_alias": "college_computer_science", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "college_computer_science", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_college_mathematics": { | |
| "task": "openaimmlu_college_mathematics", | |
| "task_alias": "college_mathematics", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "college_mathematics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_college_medicine": { | |
| "task": "openaimmlu_college_medicine", | |
| "task_alias": "college_medicine", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "college_medicine", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_college_physics": { | |
| "task": "openaimmlu_college_physics", | |
| "task_alias": "college_physics", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "college_physics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_computer_security": { | |
| "task": "openaimmlu_computer_security", | |
| "task_alias": "computer_security", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "computer_security", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_conceptual_physics": { | |
| "task": "openaimmlu_conceptual_physics", | |
| "task_alias": "conceptual_physics", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "conceptual_physics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_econometrics": { | |
| "task": "openaimmlu_econometrics", | |
| "task_alias": "econometrics", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "econometrics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_electrical_engineering": { | |
| "task": "openaimmlu_electrical_engineering", | |
| "task_alias": "electrical_engineering", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "electrical_engineering", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_elementary_mathematics": { | |
| "task": "openaimmlu_elementary_mathematics", | |
| "task_alias": "elementary_mathematics", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "elementary_mathematics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_formal_logic": { | |
| "task": "openaimmlu_formal_logic", | |
| "task_alias": "formal_logic", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "formal_logic", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_global_facts": { | |
| "task": "openaimmlu_global_facts", | |
| "task_alias": "global_facts", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "global_facts", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_biology": { | |
| "task": "openaimmlu_high_school_biology", | |
| "task_alias": "high_school_biology", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_biology", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_chemistry": { | |
| "task": "openaimmlu_high_school_chemistry", | |
| "task_alias": "high_school_chemistry", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_chemistry", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_computer_science": { | |
| "task": "openaimmlu_high_school_computer_science", | |
| "task_alias": "high_school_computer_science", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_computer_science", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_european_history": { | |
| "task": "openaimmlu_high_school_european_history", | |
| "task_alias": "high_school_european_history", | |
| "tag": "openaimmlu_humanities_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_european_history", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_geography": { | |
| "task": "openaimmlu_high_school_geography", | |
| "task_alias": "high_school_geography", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_geography", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_government_and_politics": { | |
| "task": "openaimmlu_high_school_government_and_politics", | |
| "task_alias": "high_school_government_and_politics", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_government_and_politics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_macroeconomics": { | |
| "task": "openaimmlu_high_school_macroeconomics", | |
| "task_alias": "high_school_macroeconomics", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_macroeconomics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_mathematics": { | |
| "task": "openaimmlu_high_school_mathematics", | |
| "task_alias": "high_school_mathematics", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_mathematics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_microeconomics": { | |
| "task": "openaimmlu_high_school_microeconomics", | |
| "task_alias": "high_school_microeconomics", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_microeconomics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_physics": { | |
| "task": "openaimmlu_high_school_physics", | |
| "task_alias": "high_school_physics", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_physics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_psychology": { | |
| "task": "openaimmlu_high_school_psychology", | |
| "task_alias": "high_school_psychology", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_psychology", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_statistics": { | |
| "task": "openaimmlu_high_school_statistics", | |
| "task_alias": "high_school_statistics", | |
| "tag": "openaimmlu_STEM_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_statistics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_us_history": { | |
| "task": "openaimmlu_high_school_us_history", | |
| "task_alias": "high_school_us_history", | |
| "tag": "openaimmlu_humanities_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_us_history", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_high_school_world_history": { | |
| "task": "openaimmlu_high_school_world_history", | |
| "task_alias": "high_school_world_history", | |
| "tag": "openaimmlu_humanities_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "high_school_world_history", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_human_aging": { | |
| "task": "openaimmlu_human_aging", | |
| "task_alias": "human_aging", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "human_aging", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_human_sexuality": { | |
| "task": "openaimmlu_human_sexuality", | |
| "task_alias": "human_sexuality", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "human_sexuality", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_international_law": { | |
| "task": "openaimmlu_international_law", | |
| "task_alias": "international_law", | |
| "tag": "openaimmlu_humanities_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "international_law", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_jurisprudence": { | |
| "task": "openaimmlu_jurisprudence", | |
| "task_alias": "jurisprudence", | |
| "tag": "openaimmlu_humanities_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "jurisprudence", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_logical_fallacies": { | |
| "task": "openaimmlu_logical_fallacies", | |
| "task_alias": "logical_fallacies", | |
| "tag": "openaimmlu_humanities_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "logical_fallacies", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_machine_learning": { | |
| "task": "openaimmlu_machine_learning", | |
| "task_alias": "machine_learning", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "machine_learning", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_management": { | |
| "task": "openaimmlu_management", | |
| "task_alias": "management", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "management", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_marketing": { | |
| "task": "openaimmlu_marketing", | |
| "task_alias": "marketing", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "marketing", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_medical_genetics": { | |
| "task": "openaimmlu_medical_genetics", | |
| "task_alias": "medical_genetics", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "medical_genetics", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_miscellaneous": { | |
| "task": "openaimmlu_miscellaneous", | |
| "task_alias": "miscellaneous", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "miscellaneous", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_moral_disputes": { | |
| "task": "openaimmlu_moral_disputes", | |
| "task_alias": "moral_disputes", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "moral_disputes", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_moral_scenarios": { | |
| "task": "openaimmlu_moral_scenarios", | |
| "task_alias": "moral_scenarios", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "moral_scenarios", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_nutrition": { | |
| "task": "openaimmlu_nutrition", | |
| "task_alias": "nutrition", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "nutrition", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_philosophy": { | |
| "task": "openaimmlu_philosophy", | |
| "task_alias": "philosophy", | |
| "tag": "openaimmlu_humanities_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "philosophy", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_prehistory": { | |
| "task": "openaimmlu_prehistory", | |
| "task_alias": "prehistory", | |
| "tag": "openaimmlu_humanities_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "prehistory", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_professional_accounting": { | |
| "task": "openaimmlu_professional_accounting", | |
| "task_alias": "professional_accounting", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "professional_accounting", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_professional_law": { | |
| "task": "openaimmlu_professional_law", | |
| "task_alias": "professional_law", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "professional_law", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_professional_medicine": { | |
| "task": "openaimmlu_professional_medicine", | |
| "task_alias": "professional_medicine", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "professional_medicine", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_professional_psychology": { | |
| "task": "openaimmlu_professional_psychology", | |
| "task_alias": "professional_psychology", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "professional_psychology", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_public_relations": { | |
| "task": "openaimmlu_public_relations", | |
| "task_alias": "public_relations", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "public_relations", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_security_studies": { | |
| "task": "openaimmlu_security_studies", | |
| "task_alias": "security_studies", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "security_studies", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_sociology": { | |
| "task": "openaimmlu_sociology", | |
| "task_alias": "sociology", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "sociology", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_us_foreign_policy": { | |
| "task": "openaimmlu_us_foreign_policy", | |
| "task_alias": "us_foreign_policy", | |
| "tag": "openaimmlu_social_science_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "us_foreign_policy", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_virology": { | |
| "task": "openaimmlu_virology", | |
| "task_alias": "virology", | |
| "tag": "openaimmlu_other_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "virology", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "openaimmlu_world_religions": { | |
| "task": "openaimmlu_world_religions", | |
| "task_alias": "world_religions", | |
| "tag": "openaimmlu_humanities_tasks", | |
| "dataset_path": "khalidalt/openai_mmlu_arabic", | |
| "dataset_name": "world_religions", | |
| "test_split": "test", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n\n def format_example(doc, choices):\n options = []\n for _, choice in enumerate(choices):\n options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n ar_subject = SUBJECTS[doc['Subject']]\n query = PROMPT.format(ar_subject, #doc['Subject'],\n doc['Question'],\n \"\\n\".join(options))\n return query\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n ar_label = en2ar[doc['Answer']]\n\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_ar,\n \"gold\": keys_ar.index(ar_label)\n }\n\n return out_doc\n\n return dataset.map(_process_docs) \n", | |
| "doc_to_text": "query", | |
| "doc_to_target": "gold", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| } | |
| }, | |
| "versions": { | |
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| }, | |
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| "config": { | |
| "model": "hf", | |
| "model_args": "pretrained=inceptionai/jais-adapted-7b-chat,trust_remote_code=True,cache_dir=/tmp,parallelize=False", | |
| "model_num_parameters": 7000559616, | |
| "model_dtype": "torch.float32", | |
| "model_revision": "main", | |
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| "date": 1736968038.6495116, | |
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