{ "results": { "arabicmmlu": { "acc,none": 0.6777585610515393, "acc_stderr,none": 0.0037651094938210825, "alias": "arabicmmlu" }, "arabicmmlu_humanities": { "acc,none": 0.7196802646085998, "acc_stderr,none": 0.007156852970625745, "alias": " - Humanities" }, "arabicmmlu_high_history": { "alias": " - High History", "acc,none": 0.5039473684210526, "acc_stderr,none": 0.01814828462669052 }, "arabicmmlu_high_islamic_studies": { "alias": " - High Islamic Studies", "acc,none": 0.7485029940119761, "acc_stderr,none": 0.023776124368602287 }, "arabicmmlu_high_philosophy": { "alias": " - High Philosophy", "acc,none": 0.7435897435897436, "acc_stderr,none": 0.07083413480167725 }, "arabicmmlu_islamic_studies": { "alias": " - Islamic Studies", "acc,none": 0.704225352112676, "acc_stderr,none": 0.018068660651366884 }, "arabicmmlu_middle_history": { "alias": " - Middle History", "acc,none": 0.7241379310344828, "acc_stderr,none": 0.03144712581678242 }, "arabicmmlu_middle_islamic_studies": { "alias": " - Middle Islamic Studies", "acc,none": 0.7647058823529411, "acc_stderr,none": 0.027553614467863807 }, "arabicmmlu_primary_history": { "alias": " - Primary History", "acc,none": 0.7647058823529411, "acc_stderr,none": 0.04220773659171455 }, "arabicmmlu_primary_islamic_studies": { "alias": " - Primary Islamic Studies", "acc,none": 0.8708708708708709, "acc_stderr,none": 0.010615091024310195 }, "arabicmmlu_prof_law": { "alias": " - Prof Law", "acc,none": 0.7070063694267515, "acc_stderr,none": 0.025725781937262132 }, "arabicmmlu_language": { "acc,none": 0.7053462940461726, "acc_stderr,none": 0.010675632352174308, "alias": " - Language" }, "arabicmmlu_arabic_language_(general)": { "alias": " - Arabic Language (General)", "acc,none": 0.8088235294117647, "acc_stderr,none": 0.01590829013627805 }, "arabicmmlu_arabic_language_(grammar)": { "alias": " - Arabic Language (Grammar)", "acc,none": 0.7232876712328767, "acc_stderr,none": 0.02344871747678411 }, "arabicmmlu_high_arabic_language": { "alias": " - High Arabic Language", "acc,none": 0.45384615384615384, "acc_stderr,none": 0.025242770987126177 }, "arabicmmlu_middle_arabic_language": { "alias": " - Middle Arabic Language", "acc,none": 0.8518518518518519, "acc_stderr,none": 0.06966962541673782 }, "arabicmmlu_primary_arabic_language": { "alias": " - Primary Arabic Language", "acc,none": 0.8015873015873016, "acc_stderr,none": 0.025172322396351483 }, "arabicmmlu_other": { "acc,none": 0.7089371980676329, "acc_stderr,none": 0.009115340366470213, "alias": " - Other" }, "arabicmmlu_driving_test": { "alias": " - Driving Test", "acc,none": 0.6985962014863749, "acc_stderr,none": 0.013191518335507111 }, "arabicmmlu_general_knowledge": { "alias": " - General Knowledge", "acc,none": 0.7199074074074074, "acc_stderr,none": 0.015285643798521893 }, "arabicmmlu_middle_general_knowledge": { "alias": " - Middle General Knowledge", "acc,none": 0.6802325581395349, "acc_stderr,none": 0.035665455380848116 }, "arabicmmlu_primary_general_knowledge": { "alias": " - Primary General Knowledge", "acc,none": 0.7654320987654321, "acc_stderr,none": 0.03339448023577033 }, "arabicmmlu_univ_management": { "alias": " - Univ Management", "acc,none": 0.6933333333333334, "acc_stderr,none": 0.05360292224565066 }, "arabicmmlu_social_science": { "acc,none": 0.641837899543379, "acc_stderr,none": 0.00797908211240422, "alias": " - Social Science" }, "arabicmmlu_high_civics": { "alias": " - High Civics", "acc,none": 0.4827586206896552, "acc_stderr,none": 0.05388432214060092 }, "arabicmmlu_high_economics": { "alias": " - High Economics", "acc,none": 0.625, "acc_stderr,none": 0.025551030374592384 }, "arabicmmlu_high_geography": { "alias": " - High Geography", "acc,none": 0.5770712909441233, "acc_stderr,none": 0.015341186146893518 }, "arabicmmlu_middle_civics": { "alias": " - Middle Civics", "acc,none": 0.5932203389830508, "acc_stderr,none": 0.03204451480926517 }, "arabicmmlu_middle_economics": { "alias": " - Middle Economics", "acc,none": 0.7471264367816092, "acc_stderr,none": 0.04687049503854671 }, "arabicmmlu_middle_geography": { "alias": " - Middle Geography", "acc,none": 0.7132352941176471, "acc_stderr,none": 0.02747227447323382 }, "arabicmmlu_middle_social_science": { "alias": " - Middle Social Science", "acc,none": 0.5767634854771784, "acc_stderr,none": 0.03189222523446444 }, "arabicmmlu_primary_geography": { "alias": " - Primary Geography", "acc,none": 0.7719298245614035, "acc_stderr,none": 0.05606981784761176 }, "arabicmmlu_primary_social_science": { "alias": " - Primary Social Science", "acc,none": 0.7815602836879433, "acc_stderr,none": 0.015572585115281092 }, "arabicmmlu_univ_accounting": { "alias": " - Univ Accounting", "acc,none": 0.6351351351351351, "acc_stderr,none": 0.05634270081349515 }, "arabicmmlu_univ_economics": { "alias": " - Univ Economics", "acc,none": 0.5693430656934306, "acc_stderr,none": 0.04246032224326305 }, "arabicmmlu_univ_political_science": { "alias": " - Univ Political Science", "acc,none": 0.5952380952380952, "acc_stderr,none": 0.03395252139627751 }, "arabicmmlu_stem": { "acc,none": 0.6310679611650486, "acc_stderr,none": 0.008195409873199793, "alias": " - STEM" }, "arabicmmlu_high_biology": { "alias": " - High Biology", "acc,none": 0.5095812633073101, "acc_stderr,none": 0.013322598053209577 }, "arabicmmlu_high_computer_science": { "alias": " - High Computer Science", "acc,none": 0.6934865900383141, "acc_stderr,none": 0.02859282719866765 }, "arabicmmlu_high_physics": { "alias": " - High Physics", "acc,none": 0.5176470588235295, "acc_stderr,none": 0.031353244021767535 }, "arabicmmlu_middle_computer_science": { "alias": " - Middle Computer Science", "acc,none": 0.9259259259259259, "acc_stderr,none": 0.051361129280113826 }, "arabicmmlu_middle_natural_science": { "alias": " - Middle Natural Science", "acc,none": 0.8016528925619835, "acc_stderr,none": 0.02568606613318377 }, "arabicmmlu_primary_computer_science": { "alias": " - Primary Computer Science", "acc,none": 0.7473684210526316, "acc_stderr,none": 0.031606782497111685 }, "arabicmmlu_primary_math": { "alias": " - Primary Math", "acc,none": 0.6772616136919315, "acc_stderr,none": 0.023145867389961022 }, "arabicmmlu_primary_natural_science": { "alias": " - Primary Natural Science", "acc,none": 0.8839285714285714, "acc_stderr,none": 0.017500435136664095 }, "arabicmmlu_univ_computer_science": { "alias": " - Univ Computer Science", "acc,none": 0.765625, "acc_stderr,none": 0.053369535239372906 } }, "groups": { "arabicmmlu": { "acc,none": 0.6777585610515393, "acc_stderr,none": 0.0037651094938210825, "alias": "arabicmmlu" }, "arabicmmlu_humanities": { "acc,none": 0.7196802646085998, "acc_stderr,none": 0.007156852970625745, "alias": " - Humanities" }, "arabicmmlu_language": { "acc,none": 0.7053462940461726, "acc_stderr,none": 0.010675632352174308, "alias": " - Language" }, "arabicmmlu_other": { "acc,none": 0.7089371980676329, "acc_stderr,none": 0.009115340366470213, "alias": " - Other" }, "arabicmmlu_social_science": { "acc,none": 0.641837899543379, "acc_stderr,none": 0.00797908211240422, "alias": " - Social Science" }, "arabicmmlu_stem": { "acc,none": 0.6310679611650486, "acc_stderr,none": 0.008195409873199793, "alias": " - STEM" } }, "group_subtasks": { "arabicmmlu_language": [ "arabicmmlu_high_arabic_language", "arabicmmlu_arabic_language_(grammar)", "arabicmmlu_middle_arabic_language", "arabicmmlu_arabic_language_(general)", "arabicmmlu_primary_arabic_language" ], "arabicmmlu_stem": [ "arabicmmlu_high_computer_science", "arabicmmlu_primary_math", "arabicmmlu_high_biology", "arabicmmlu_primary_computer_science", "arabicmmlu_middle_natural_science", "arabicmmlu_high_physics", "arabicmmlu_middle_computer_science", "arabicmmlu_univ_computer_science", "arabicmmlu_primary_natural_science" ], "arabicmmlu_humanities": [ "arabicmmlu_prof_law", "arabicmmlu_middle_islamic_studies", "arabicmmlu_high_philosophy", "arabicmmlu_high_islamic_studies", "arabicmmlu_islamic_studies", "arabicmmlu_high_history", "arabicmmlu_primary_islamic_studies", "arabicmmlu_middle_history", "arabicmmlu_primary_history" ], "arabicmmlu_social_science": [ "arabicmmlu_middle_social_science", "arabicmmlu_univ_economics", "arabicmmlu_univ_accounting", "arabicmmlu_high_civics", "arabicmmlu_high_economics", "arabicmmlu_middle_geography", "arabicmmlu_primary_geography", "arabicmmlu_middle_civics", "arabicmmlu_high_geography", "arabicmmlu_middle_economics", "arabicmmlu_univ_political_science", "arabicmmlu_primary_social_science" ], "arabicmmlu_other": [ "arabicmmlu_primary_general_knowledge", "arabicmmlu_general_knowledge", "arabicmmlu_middle_general_knowledge", "arabicmmlu_univ_management", "arabicmmlu_driving_test" ], "arabicmmlu": [ "arabicmmlu_other", "arabicmmlu_social_science", "arabicmmlu_humanities", "arabicmmlu_stem", "arabicmmlu_language" ] }, "configs": { "arabicmmlu_arabic_language_(general)": { "task": "arabicmmlu_arabic_language_(general)", "task_alias": "Arabic Language (General)", "tag": "arabicmmlu_language_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Arabic Language (General)", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_arabic_language_(grammar)": { "task": "arabicmmlu_arabic_language_(grammar)", "task_alias": "Arabic Language (Grammar)", "tag": "arabicmmlu_language_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Arabic Language (Grammar)", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_driving_test": { "task": "arabicmmlu_driving_test", "task_alias": "Driving Test", "tag": "arabicmmlu_other_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Driving Test", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_general_knowledge": { "task": "arabicmmlu_general_knowledge", "task_alias": "General Knowledge", "tag": "arabicmmlu_other_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "General Knowledge", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_high_arabic_language": { "task": "arabicmmlu_high_arabic_language", "task_alias": "High Arabic Language", "tag": "arabicmmlu_language_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "High Arabic Language", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_high_biology": { "task": "arabicmmlu_high_biology", "task_alias": "High Biology", "tag": "arabicmmlu_stem_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "High Biology", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_high_civics": { "task": "arabicmmlu_high_civics", "task_alias": "High Civics", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "High Civics", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_high_computer_science": { "task": "arabicmmlu_high_computer_science", "task_alias": "High Computer Science", "tag": "arabicmmlu_stem_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "High Computer Science", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_high_economics": { "task": "arabicmmlu_high_economics", "task_alias": "High Economics", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "High Economics", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_high_geography": { "task": "arabicmmlu_high_geography", "task_alias": "High Geography", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "High Geography", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_high_history": { "task": "arabicmmlu_high_history", "task_alias": "High History", "tag": "arabicmmlu_humanities_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "High History", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_high_islamic_studies": { "task": "arabicmmlu_high_islamic_studies", "task_alias": "High Islamic Studies", "tag": "arabicmmlu_humanities_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "High Islamic Studies", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_high_philosophy": { "task": "arabicmmlu_high_philosophy", "task_alias": "High Philosophy", "tag": "arabicmmlu_humanities_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "High Philosophy", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_high_physics": { "task": "arabicmmlu_high_physics", "task_alias": "High Physics", "tag": "arabicmmlu_stem_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "High Physics", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_islamic_studies": { "task": "arabicmmlu_islamic_studies", "task_alias": "Islamic Studies", "tag": "arabicmmlu_humanities_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Islamic Studies", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_middle_arabic_language": { "task": "arabicmmlu_middle_arabic_language", "task_alias": "Middle Arabic Language", "tag": "arabicmmlu_language_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Middle Arabic Language", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_middle_civics": { "task": "arabicmmlu_middle_civics", "task_alias": "Middle Civics", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Middle Civics", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_middle_computer_science": { "task": "arabicmmlu_middle_computer_science", "task_alias": "Middle Computer Science", "tag": "arabicmmlu_stem_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Middle Computer Science", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_middle_economics": { "task": "arabicmmlu_middle_economics", "task_alias": "Middle Economics", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Middle Economics", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_middle_general_knowledge": { "task": "arabicmmlu_middle_general_knowledge", "task_alias": "Middle General Knowledge", "tag": "arabicmmlu_other_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Middle General Knowledge", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_middle_geography": { "task": "arabicmmlu_middle_geography", "task_alias": "Middle Geography", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Middle Geography", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_middle_history": { "task": "arabicmmlu_middle_history", "task_alias": "Middle History", "tag": "arabicmmlu_humanities_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Middle History", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_middle_islamic_studies": { "task": "arabicmmlu_middle_islamic_studies", "task_alias": "Middle Islamic Studies", "tag": "arabicmmlu_humanities_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Middle Islamic Studies", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_middle_natural_science": { "task": "arabicmmlu_middle_natural_science", "task_alias": "Middle Natural Science", "tag": "arabicmmlu_stem_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Middle Natural Science", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_middle_social_science": { "task": "arabicmmlu_middle_social_science", "task_alias": "Middle Social Science", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Middle Social Science", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_primary_arabic_language": { "task": "arabicmmlu_primary_arabic_language", "task_alias": "Primary Arabic Language", "tag": "arabicmmlu_language_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Primary Arabic Language", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_primary_computer_science": { "task": "arabicmmlu_primary_computer_science", "task_alias": "Primary Computer Science", "tag": "arabicmmlu_stem_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Primary Computer Science", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_primary_general_knowledge": { "task": "arabicmmlu_primary_general_knowledge", "task_alias": "Primary General Knowledge", "tag": "arabicmmlu_other_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Primary General Knowledge", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_primary_geography": { "task": "arabicmmlu_primary_geography", "task_alias": "Primary Geography", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Primary Geography", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_primary_history": { "task": "arabicmmlu_primary_history", "task_alias": "Primary History", "tag": "arabicmmlu_humanities_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Primary History", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_primary_islamic_studies": { "task": "arabicmmlu_primary_islamic_studies", "task_alias": "Primary Islamic Studies", "tag": "arabicmmlu_humanities_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Primary Islamic Studies", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_primary_math": { "task": "arabicmmlu_primary_math", "task_alias": "Primary Math", "tag": "arabicmmlu_stem_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Primary Math", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_primary_natural_science": { "task": "arabicmmlu_primary_natural_science", "task_alias": "Primary Natural Science", "tag": "arabicmmlu_stem_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Primary Natural Science", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_primary_social_science": { "task": "arabicmmlu_primary_social_science", "task_alias": "Primary Social Science", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Primary Social Science", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_prof_law": { "task": "arabicmmlu_prof_law", "task_alias": "Prof Law", "tag": "arabicmmlu_humanities_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Prof Law", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_univ_accounting": { "task": "arabicmmlu_univ_accounting", "task_alias": "Univ Accounting", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Univ Accounting", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_univ_computer_science": { "task": "arabicmmlu_univ_computer_science", "task_alias": "Univ Computer Science", "tag": "arabicmmlu_stem_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Univ Computer Science", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_univ_economics": { "task": "arabicmmlu_univ_economics", "task_alias": "Univ Economics", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Univ Economics", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_univ_management": { "task": "arabicmmlu_univ_management", "task_alias": "Univ Management", "tag": "arabicmmlu_other_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Univ Management", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } }, "arabicmmlu_univ_political_science": { "task": "arabicmmlu_univ_political_science", "task_alias": "Univ Political Science", "tag": "arabicmmlu_social_science_tasks", "dataset_path": "yazeed7/ArabicMMLU", "dataset_name": "Univ Political Science", "test_split": "test", "fewshot_split": "dev", "doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n", "doc_to_target": "Answer Key", "doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n" }, "num_fewshot": 0, "metric_list": [ { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "multiple_choice", "repeats": 1, "should_decontaminate": false, "metadata": { "version": 0.0 } } }, "versions": { "arabicmmlu": 0, "arabicmmlu_arabic_language_(general)": 0.0, "arabicmmlu_arabic_language_(grammar)": 0.0, "arabicmmlu_driving_test": 0.0, "arabicmmlu_general_knowledge": 0.0, "arabicmmlu_high_arabic_language": 0.0, "arabicmmlu_high_biology": 0.0, "arabicmmlu_high_civics": 0.0, "arabicmmlu_high_computer_science": 0.0, "arabicmmlu_high_economics": 0.0, "arabicmmlu_high_geography": 0.0, "arabicmmlu_high_history": 0.0, "arabicmmlu_high_islamic_studies": 0.0, "arabicmmlu_high_philosophy": 0.0, "arabicmmlu_high_physics": 0.0, "arabicmmlu_humanities": 0, "arabicmmlu_islamic_studies": 0.0, "arabicmmlu_language": 0, "arabicmmlu_middle_arabic_language": 0.0, "arabicmmlu_middle_civics": 0.0, "arabicmmlu_middle_computer_science": 0.0, "arabicmmlu_middle_economics": 0.0, "arabicmmlu_middle_general_knowledge": 0.0, "arabicmmlu_middle_geography": 0.0, "arabicmmlu_middle_history": 0.0, "arabicmmlu_middle_islamic_studies": 0.0, "arabicmmlu_middle_natural_science": 0.0, "arabicmmlu_middle_social_science": 0.0, "arabicmmlu_other": 0, "arabicmmlu_primary_arabic_language": 0.0, "arabicmmlu_primary_computer_science": 0.0, "arabicmmlu_primary_general_knowledge": 0.0, "arabicmmlu_primary_geography": 0.0, "arabicmmlu_primary_history": 0.0, "arabicmmlu_primary_islamic_studies": 0.0, "arabicmmlu_primary_math": 0.0, "arabicmmlu_primary_natural_science": 0.0, "arabicmmlu_primary_social_science": 0.0, "arabicmmlu_prof_law": 0.0, "arabicmmlu_social_science": 0, "arabicmmlu_stem": 0, "arabicmmlu_univ_accounting": 0.0, "arabicmmlu_univ_computer_science": 0.0, "arabicmmlu_univ_economics": 0.0, "arabicmmlu_univ_management": 0.0, "arabicmmlu_univ_political_science": 0.0 }, "n-shot": { "arabicmmlu_arabic_language_(general)": 0, "arabicmmlu_arabic_language_(grammar)": 0, "arabicmmlu_driving_test": 0, "arabicmmlu_general_knowledge": 0, "arabicmmlu_high_arabic_language": 0, "arabicmmlu_high_biology": 0, "arabicmmlu_high_civics": 0, "arabicmmlu_high_computer_science": 0, "arabicmmlu_high_economics": 0, "arabicmmlu_high_geography": 0, "arabicmmlu_high_history": 0, "arabicmmlu_high_islamic_studies": 0, "arabicmmlu_high_philosophy": 0, "arabicmmlu_high_physics": 0, "arabicmmlu_islamic_studies": 0, "arabicmmlu_middle_arabic_language": 0, "arabicmmlu_middle_civics": 0, "arabicmmlu_middle_computer_science": 0, "arabicmmlu_middle_economics": 0, "arabicmmlu_middle_general_knowledge": 0, "arabicmmlu_middle_geography": 0, "arabicmmlu_middle_history": 0, "arabicmmlu_middle_islamic_studies": 0, "arabicmmlu_middle_natural_science": 0, "arabicmmlu_middle_social_science": 0, "arabicmmlu_primary_arabic_language": 0, "arabicmmlu_primary_computer_science": 0, "arabicmmlu_primary_general_knowledge": 0, "arabicmmlu_primary_geography": 0, "arabicmmlu_primary_history": 0, "arabicmmlu_primary_islamic_studies": 0, "arabicmmlu_primary_math": 0, "arabicmmlu_primary_natural_science": 0, "arabicmmlu_primary_social_science": 0, "arabicmmlu_prof_law": 0, "arabicmmlu_univ_accounting": 0, "arabicmmlu_univ_computer_science": 0, "arabicmmlu_univ_economics": 0, "arabicmmlu_univ_management": 0, "arabicmmlu_univ_political_science": 0 }, "higher_is_better": { "arabicmmlu": { "acc": true }, "arabicmmlu_arabic_language_(general)": { "acc": true }, "arabicmmlu_arabic_language_(grammar)": { "acc": true }, "arabicmmlu_driving_test": { "acc": true }, "arabicmmlu_general_knowledge": { "acc": true }, "arabicmmlu_high_arabic_language": { "acc": true }, "arabicmmlu_high_biology": { "acc": true }, "arabicmmlu_high_civics": { "acc": true }, "arabicmmlu_high_computer_science": { "acc": true }, "arabicmmlu_high_economics": { "acc": true }, "arabicmmlu_high_geography": { "acc": true }, "arabicmmlu_high_history": { "acc": true }, "arabicmmlu_high_islamic_studies": { "acc": true }, "arabicmmlu_high_philosophy": { "acc": true }, "arabicmmlu_high_physics": { "acc": true }, "arabicmmlu_humanities": { "acc": true }, "arabicmmlu_islamic_studies": { "acc": true }, "arabicmmlu_language": { "acc": true }, "arabicmmlu_middle_arabic_language": { "acc": true }, "arabicmmlu_middle_civics": { "acc": true }, "arabicmmlu_middle_computer_science": { "acc": true }, "arabicmmlu_middle_economics": { "acc": true }, "arabicmmlu_middle_general_knowledge": { "acc": 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12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 48\nOn-line CPU(s) list: 0-47\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V13 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4890.87\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 24 MiB (48 instances)\nL3 cache: 192 MiB (6 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect", 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