{ "results": { "arabicmmlu": { "acc,none": 0.5043237634036666, "acc_stderr,none": 0.004042363470895757, "alias": "arabicmmlu" }, "arabicmmlu_humanities": { "acc,none": 0.5052370452039692, "acc_stderr,none": 0.00790960602679391, "alias": " - Humanities" }, "arabicmmlu_high_history": { "alias": " - High History", "acc,none": 0.3368421052631579, "acc_stderr,none": 0.017155396919294835 }, "arabicmmlu_high_islamic_studies": { "alias": " - High Islamic Studies", "acc,none": 0.6407185628742516, "acc_stderr,none": 0.026292321014549997 }, "arabicmmlu_high_philosophy": { "alias": " - High Philosophy", "acc,none": 0.48717948717948717, "acc_stderr,none": 0.08108404256842 }, "arabicmmlu_islamic_studies": { "alias": " - Islamic Studies", "acc,none": 0.3317683881064163, "acc_stderr,none": 0.018641062838831428 }, "arabicmmlu_middle_history": { "alias": " - Middle History", "acc,none": 0.49261083743842365, "acc_stderr,none": 0.035176035403610084 }, "arabicmmlu_middle_islamic_studies": { "alias": " - Middle Islamic Studies", "acc,none": 0.6134453781512605, "acc_stderr,none": 0.03163145807552378 }, "arabicmmlu_primary_history": { "alias": " - Primary History", "acc,none": 0.46078431372549017, "acc_stderr,none": 0.04959859966384181 }, "arabicmmlu_primary_islamic_studies": { "alias": " - Primary Islamic Studies", "acc,none": 0.6926926926926927, "acc_stderr,none": 0.014604660845760144 }, "arabicmmlu_prof_law": { "alias": " - Prof Law", "acc,none": 0.4681528662420382, "acc_stderr,none": 0.028204284454138768 }, "arabicmmlu_language": { "acc,none": 0.4775212636695018, "acc_stderr,none": 0.012004811696820014, "alias": " - Language" }, "arabicmmlu_arabic_language_(general)": { "alias": " - Arabic Language (General)", "acc,none": 0.5980392156862745, "acc_stderr,none": 0.01983517648437538 }, "arabicmmlu_arabic_language_(grammar)": { "alias": " - Arabic Language (Grammar)", "acc,none": 0.34794520547945207, "acc_stderr,none": 0.024965874481689576 }, "arabicmmlu_high_arabic_language": { "alias": " - High Arabic Language", "acc,none": 0.3641025641025641, "acc_stderr,none": 0.02439667298509477 }, "arabicmmlu_middle_arabic_language": { "alias": " - Middle Arabic Language", "acc,none": 0.6296296296296297, "acc_stderr,none": 0.09470524295495535 }, "arabicmmlu_primary_arabic_language": { "alias": " - Primary Arabic Language", "acc,none": 0.5317460317460317, "acc_stderr,none": 0.03149604347936578 }, "arabicmmlu_other": { "acc,none": 0.5628019323671497, "acc_stderr,none": 0.009820739967892693, "alias": " - Other" }, "arabicmmlu_driving_test": { "alias": " - Driving Test", "acc,none": 0.620148637489678, "acc_stderr,none": 0.01395282207034666 }, "arabicmmlu_general_knowledge": { "alias": " - General Knowledge", "acc,none": 0.45023148148148145, "acc_stderr,none": 0.016935673216772293 }, "arabicmmlu_middle_general_knowledge": { "alias": " - Middle General Knowledge", "acc,none": 0.5930232558139535, "acc_stderr,none": 0.03756839173779933 }, "arabicmmlu_primary_general_knowledge": { "alias": " - Primary General Knowledge", "acc,none": 0.6481481481481481, "acc_stderr,none": 0.037636057624863876 }, "arabicmmlu_univ_management": { "alias": " - Univ Management", "acc,none": 0.68, "acc_stderr,none": 0.05422675115236518 }, "arabicmmlu_social_science": { "acc,none": 0.4994292237442922, "acc_stderr,none": 0.008286856287550251, "alias": " - Social Science" }, "arabicmmlu_high_civics": { "alias": " - High Civics", "acc,none": 0.42528735632183906, "acc_stderr,none": 0.05331106836455265 }, "arabicmmlu_high_economics": { "alias": " - High Economics", "acc,none": 0.5222222222222223, "acc_stderr,none": 0.026362914614329245 }, "arabicmmlu_high_geography": { "alias": " - High Geography", "acc,none": 0.3988439306358382, "acc_stderr,none": 0.015205676046200057 }, "arabicmmlu_middle_civics": { "alias": " - Middle Civics", "acc,none": 0.3686440677966102, "acc_stderr,none": 0.0314707306823461 }, "arabicmmlu_middle_economics": { "alias": " - Middle Economics", "acc,none": 0.6551724137931034, "acc_stderr,none": 0.05125421389342353 }, "arabicmmlu_middle_geography": { "alias": " - Middle Geography", "acc,none": 0.5698529411764706, "acc_stderr,none": 0.030074971917302875 }, "arabicmmlu_middle_social_science": { "alias": " - Middle Social Science", "acc,none": 0.4854771784232365, "acc_stderr,none": 0.03226124401232391 }, "arabicmmlu_primary_geography": { "alias": " - Primary Geography", "acc,none": 0.543859649122807, "acc_stderr,none": 0.0665577530069649 }, "arabicmmlu_primary_social_science": { "alias": " - Primary Social Science", "acc,none": 0.6524822695035462, "acc_stderr,none": 0.017946778859462872 }, "arabicmmlu_univ_accounting": { "alias": " - Univ Accounting", "acc,none": 0.5405405405405406, "acc_stderr,none": 0.05832789513012364 }, "arabicmmlu_univ_economics": { "alias": " - Univ Economics", "acc,none": 0.48175182481751827, "acc_stderr,none": 0.04284608260823147 }, "arabicmmlu_univ_political_science": { "alias": " - Univ Political Science", "acc,none": 0.4666666666666667, "acc_stderr,none": 0.034508780443504965 }, "arabicmmlu_stem": { "acc,none": 0.47698089570936425, "acc_stderr,none": 0.008646289649970346, "alias": " - STEM" }, "arabicmmlu_high_biology": { "alias": " - High Biology", "acc,none": 0.38892831795599714, "acc_stderr,none": 0.012992105378448731 }, "arabicmmlu_high_computer_science": { "alias": " - High Computer Science", "acc,none": 0.49808429118773945, "acc_stderr,none": 0.031008456046434162 }, "arabicmmlu_high_physics": { "alias": " - High Physics", "acc,none": 0.3803921568627451, "acc_stderr,none": 0.03046192691828629 }, "arabicmmlu_middle_computer_science": { "alias": " - Middle Computer Science", "acc,none": 0.5555555555555556, "acc_stderr,none": 0.09745089103411436 }, "arabicmmlu_middle_natural_science": { "alias": " - Middle Natural Science", "acc,none": 0.5495867768595041, "acc_stderr,none": 0.03204905158847432 }, "arabicmmlu_primary_computer_science": { "alias": " - Primary Computer Science", "acc,none": 0.7157894736842105, "acc_stderr,none": 0.03280815673574656 }, "arabicmmlu_primary_math": { "alias": " - Primary Math", "acc,none": 0.5232273838630807, "acc_stderr,none": 0.02472696435617918 }, "arabicmmlu_primary_natural_science": { "alias": " - Primary Natural Science", "acc,none": 0.6488095238095238, "acc_stderr,none": 0.02607999894833243 }, "arabicmmlu_univ_computer_science": { "alias": " - Univ Computer Science", "acc,none": 0.5, "acc_stderr,none": 0.06299407883487121 } }, "groups": { "arabicmmlu": { "acc,none": 0.5043237634036666, "acc_stderr,none": 0.004042363470895757, "alias": "arabicmmlu" }, "arabicmmlu_humanities": { "acc,none": 0.5052370452039692, "acc_stderr,none": 0.00790960602679391, "alias": " - Humanities" }, "arabicmmlu_language": { "acc,none": 0.4775212636695018, "acc_stderr,none": 0.012004811696820014, "alias": " - Language" }, "arabicmmlu_other": { "acc,none": 0.5628019323671497, "acc_stderr,none": 0.009820739967892693, "alias": " - Other" }, "arabicmmlu_social_science": { "acc,none": 0.4994292237442922, "acc_stderr,none": 0.008286856287550251, "alias": " - Social Science" }, "arabicmmlu_stem": { "acc,none": 0.47698089570936425, "acc_stderr,none": 0.008646289649970346, "alias": " - STEM" } }, "group_subtasks": { "arabicmmlu_language": [ "arabicmmlu_primary_arabic_language", "arabicmmlu_arabic_language_(grammar)", "arabicmmlu_high_arabic_language", "arabicmmlu_middle_arabic_language", "arabicmmlu_arabic_language_(general)" ], "arabicmmlu_stem": [ "arabicmmlu_middle_natural_science", "arabicmmlu_univ_computer_science", "arabicmmlu_high_physics", "arabicmmlu_high_biology", "arabicmmlu_middle_computer_science", "arabicmmlu_primary_natural_science", "arabicmmlu_primary_computer_science", "arabicmmlu_high_computer_science", "arabicmmlu_primary_math" ], "arabicmmlu_humanities": [ "arabicmmlu_middle_islamic_studies", "arabicmmlu_prof_law", "arabicmmlu_primary_islamic_studies", "arabicmmlu_high_history", "arabicmmlu_middle_history", "arabicmmlu_islamic_studies", "arabicmmlu_high_islamic_studies", "arabicmmlu_high_philosophy", "arabicmmlu_primary_history" ], "arabicmmlu_social_science": [ "arabicmmlu_primary_geography", "arabicmmlu_middle_social_science", "arabicmmlu_high_civics", "arabicmmlu_middle_geography", "arabicmmlu_primary_social_science", "arabicmmlu_middle_economics", "arabicmmlu_middle_civics", "arabicmmlu_univ_economics", "arabicmmlu_univ_accounting", "arabicmmlu_univ_political_science", "arabicmmlu_high_geography", "arabicmmlu_high_economics" ], "arabicmmlu_other": [ "arabicmmlu_general_knowledge", "arabicmmlu_driving_test", "arabicmmlu_univ_management", "arabicmmlu_primary_general_knowledge", "arabicmmlu_middle_general_knowledge" ], "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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"original": 612, "effective": 612 } }, "config": { "model": "hf", "model_args": "pretrained=mistralai/Mistral-Small-Instruct-2409,trust_remote_code=True,cache_dir=/tmp,parallelize=False", "model_num_parameters": 22247282688, "model_dtype": "torch.bfloat16", "model_revision": "main", "model_sha": "8012044390bdc1c6d8ab162f5416220f43bf517b", "batch_size": "auto", "batch_sizes": [ 16 ], "device": null, "use_cache": null, "limit": null, "bootstrap_iters": 100000, "gen_kwargs": null, "random_seed": 0, "numpy_seed": 1234, "torch_seed": 1234, "fewshot_seed": 1234 }, "git_hash": "5e10e017", "date": 1736972751.2143774, "pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 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", "transformers_version": "4.48.0", "upper_git_hash": "2e5cd5395faf76fea1afc96dd0f7161a9d3aa145", "tokenizer_pad_token": [ "", "2" ], "tokenizer_eos_token": [ "", "2" ], "tokenizer_bos_token": [ "", "1" ], "eot_token_id": 2, "max_length": 32768, "task_hashes": {}, "model_source": "hf", "model_name": "mistralai/Mistral-Small-Instruct-2409", "model_name_sanitized": "mistralai__Mistral-Small-Instruct-2409", "system_instruction": null, "system_instruction_sha": null, "fewshot_as_multiturn": false, "chat_template": null, "chat_template_sha": null, "start_time": 14232.929786561, "end_time": 14765.426940165, "total_evaluation_time_seconds": "532.4971536039993" }