diff --git "a/evaluations/ar/AceGPT-v2-32B-Chat/arabicmmlu_0_shot.json" "b/evaluations/ar/AceGPT-v2-32B-Chat/arabicmmlu_0_shot.json" new file mode 100644--- /dev/null +++ "b/evaluations/ar/AceGPT-v2-32B-Chat/arabicmmlu_0_shot.json" @@ -0,0 +1,2051 @@ +{ + "results": { + "arabicmmlu": { + "acc,none": 0.6830162573503978, + "acc_stderr,none": 0.0037666673237025995, + "alias": "arabicmmlu" + }, + "arabicmmlu_humanities": { + "acc,none": 0.698180815876516, + "acc_stderr,none": 0.0074113813583826975, + "alias": " - Humanities" + }, + "arabicmmlu_high_history": { + "alias": " - High History", + "acc,none": 0.5578947368421052, + "acc_stderr,none": 0.01802677701787401 + }, + "arabicmmlu_high_islamic_studies": { + "alias": " - High Islamic Studies", + "acc,none": 0.7365269461077845, + "acc_stderr,none": 0.02414016899389538 + }, + "arabicmmlu_high_philosophy": { + "alias": " - High Philosophy", + "acc,none": 0.6410256410256411, + "acc_stderr,none": 0.07781756136754926 + }, + "arabicmmlu_islamic_studies": { + "alias": " - Islamic Studies", + "acc,none": 0.5915492957746479, + "acc_stderr,none": 0.019460543090359293 + }, + "arabicmmlu_middle_history": { + "alias": " - Middle History", + "acc,none": 0.7142857142857143, + "acc_stderr,none": 0.03178529710642749 + }, + "arabicmmlu_middle_islamic_studies": { + "alias": " - Middle Islamic Studies", + "acc,none": 0.7142857142857143, + "acc_stderr,none": 0.029344572500634363 + }, + "arabicmmlu_primary_history": { + "alias": " - Primary History", + "acc,none": 0.6764705882352942, + "acc_stderr,none": 0.0465501041131961 + }, + "arabicmmlu_primary_islamic_studies": { + "alias": " - Primary Islamic Studies", + "acc,none": 0.8348348348348348, + "acc_stderr,none": 0.01175423146342287 + }, + "arabicmmlu_prof_law": { + "alias": " - Prof Law", + "acc,none": 0.7707006369426752, + "acc_stderr,none": 0.02376140487281449 + }, + "arabicmmlu_language": { + "acc,none": 0.6877278250303767, + "acc_stderr,none": 0.010897190392354756, + "alias": " - Language" + }, + "arabicmmlu_arabic_language_(general)": { + "alias": " - Arabic Language (General)", + "acc,none": 0.7990196078431373, + "acc_stderr,none": 0.01621193888965557 + }, + "arabicmmlu_arabic_language_(grammar)": { + "alias": " - Arabic Language (Grammar)", + "acc,none": 0.726027397260274, + "acc_stderr,none": 0.023376494233709237 + }, + "arabicmmlu_high_arabic_language": { + "alias": " - High Arabic Language", + "acc,none": 0.441025641025641, + "acc_stderr,none": 0.025174048384000766 + }, + "arabicmmlu_middle_arabic_language": { + "alias": " - Middle Arabic Language", + "acc,none": 0.8148148148148148, + "acc_stderr,none": 0.07618086585254093 + }, + "arabicmmlu_primary_arabic_language": { + "alias": " - Primary Arabic Language", + "acc,none": 0.7301587301587301, + "acc_stderr,none": 0.028017279737180052 + }, + "arabicmmlu_other": { + "acc,none": 0.7210144927536232, + "acc_stderr,none": 0.008956944496736811, + "alias": " - Other" + }, + "arabicmmlu_driving_test": { + "alias": " - Driving Test", + "acc,none": 0.7506193228736582, + "acc_stderr,none": 0.012437943646387221 + }, + "arabicmmlu_general_knowledge": { + "alias": " - General Knowledge", + "acc,none": 0.6574074074074074, + "acc_stderr,none": 0.016154773861994782 + }, + "arabicmmlu_middle_general_knowledge": { + "alias": " - Middle General Knowledge", + "acc,none": 0.7441860465116279, + "acc_stderr,none": 0.03336605189761063 + }, + "arabicmmlu_primary_general_knowledge": { + "alias": " - Primary General Knowledge", + "acc,none": 0.7777777777777778, + "acc_stderr,none": 0.0327648791455327 + }, + "arabicmmlu_univ_management": { + "alias": " - Univ Management", + "acc,none": 0.8, + "acc_stderr,none": 0.046499055497527676 + }, + "arabicmmlu_social_science": { + "acc,none": 0.6726598173515982, + "acc_stderr,none": 0.007798259846846906, + "alias": " - Social Science" + }, + "arabicmmlu_high_civics": { + "alias": " - High Civics", + "acc,none": 0.5057471264367817, + "acc_stderr,none": 0.053912824825556656 + }, + "arabicmmlu_high_economics": { + "alias": " - High Economics", + "acc,none": 0.7111111111111111, + "acc_stderr,none": 0.023921418402752255 + }, + "arabicmmlu_high_geography": { + "alias": " - High Geography", + "acc,none": 0.6040462427745664, + "acc_stderr,none": 0.015186858609050091 + }, + "arabicmmlu_middle_civics": { + "alias": " - Middle Civics", + "acc,none": 0.6059322033898306, + "acc_stderr,none": 0.03187598097180376 + }, + "arabicmmlu_middle_economics": { + "alias": " - Middle Economics", + "acc,none": 0.8160919540229885, + "acc_stderr,none": 0.04177540678018987 + }, + "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": 0.5518672199170125, + "acc_stderr,none": 0.032100739315089555 + }, + "arabicmmlu_primary_geography": { + "alias": " - Primary Geography", + "acc,none": 0.7368421052631579, + "acc_stderr,none": 0.058843894144731304 + }, + "arabicmmlu_primary_social_science": { + "alias": " - Primary Social Science", + "acc,none": 0.8056737588652483, + "acc_stderr,none": 0.014912793524753134 + }, + "arabicmmlu_univ_accounting": { + "alias": " - Univ Accounting", + "acc,none": 0.6756756756756757, + "acc_stderr,none": 0.05478951716752587 + }, + "arabicmmlu_univ_economics": { + "alias": " - Univ Economics", + "acc,none": 0.6496350364963503, + "acc_stderr,none": 0.040909634620704266 + }, + "arabicmmlu_univ_political_science": { + "alias": " - Univ Political Science", + "acc,none": 0.6666666666666666, + "acc_stderr,none": 0.03260773253630123 + }, + "arabicmmlu_stem": { + "acc,none": 0.6451612903225806, + "acc_stderr,none": 0.008155612741868946, + "alias": " - STEM" + }, + "arabicmmlu_high_biology": { + "alias": " - High Biology", + "acc,none": 0.525195173882186, + "acc_stderr,none": 0.013308116628249263 + }, + "arabicmmlu_high_computer_science": { + "alias": " - High Computer Science", + "acc,none": 0.7164750957854407, + "acc_stderr,none": 0.027951780795387696 + }, + "arabicmmlu_high_physics": { + "alias": " - High Physics", + "acc,none": 0.5764705882352941, + "acc_stderr,none": 0.03100369860682665 + }, + "arabicmmlu_middle_computer_science": { + "alias": " - Middle Computer Science", + "acc,none": 0.8518518518518519, + "acc_stderr,none": 0.06966962541673782 + }, + "arabicmmlu_middle_natural_science": { + "alias": " - Middle Natural Science", + "acc,none": 0.8140495867768595, + "acc_stderr,none": 0.025061985980100218 + }, + "arabicmmlu_primary_computer_science": { + "alias": " - Primary Computer Science", + "acc,none": 0.7315789473684211, + "acc_stderr,none": 0.032233538609655936 + }, + "arabicmmlu_primary_math": { + "alias": " - Primary Math", + "acc,none": 0.684596577017115, + "acc_stderr,none": 0.023004906965559055 + }, + "arabicmmlu_primary_natural_science": { + "alias": " - Primary Natural Science", + "acc,none": 0.8988095238095238, + "acc_stderr,none": 0.01647711789379545 + }, + "arabicmmlu_univ_computer_science": { + "alias": " - Univ Computer Science", + "acc,none": 0.703125, + "acc_stderr,none": 0.05756159356351619 + } + }, + "groups": { + "arabicmmlu": { + "acc,none": 0.6830162573503978, + "acc_stderr,none": 0.0037666673237025995, + "alias": "arabicmmlu" + }, + "arabicmmlu_humanities": { + "acc,none": 0.698180815876516, + "acc_stderr,none": 0.0074113813583826975, + "alias": " - Humanities" + }, + "arabicmmlu_language": { + "acc,none": 0.6877278250303767, + "acc_stderr,none": 0.010897190392354756, + "alias": " - Language" + }, + "arabicmmlu_other": { + "acc,none": 0.7210144927536232, + "acc_stderr,none": 0.008956944496736811, + "alias": " - Other" + }, + "arabicmmlu_social_science": { + "acc,none": 0.6726598173515982, + "acc_stderr,none": 0.007798259846846906, + "alias": " - Social Science" + }, + "arabicmmlu_stem": { + "acc,none": 0.6451612903225806, + "acc_stderr,none": 0.008155612741868946, + "alias": " - STEM" + } + }, + "group_subtasks": { + "arabicmmlu_language": [ + "arabicmmlu_primary_arabic_language", + "arabicmmlu_middle_arabic_language", + "arabicmmlu_high_arabic_language", + "arabicmmlu_arabic_language_(grammar)", + "arabicmmlu_arabic_language_(general)" + ], + "arabicmmlu_stem": [ + "arabicmmlu_high_physics", + "arabicmmlu_primary_math", + "arabicmmlu_high_computer_science", + "arabicmmlu_middle_natural_science", + "arabicmmlu_high_biology", + "arabicmmlu_primary_computer_science", + "arabicmmlu_primary_natural_science", + "arabicmmlu_univ_computer_science", + "arabicmmlu_middle_computer_science" + ], + "arabicmmlu_humanities": [ + "arabicmmlu_prof_law", + "arabicmmlu_middle_history", + "arabicmmlu_primary_islamic_studies", + "arabicmmlu_high_philosophy", + "arabicmmlu_high_islamic_studies", + "arabicmmlu_islamic_studies", + "arabicmmlu_primary_history", + "arabicmmlu_high_history", + "arabicmmlu_middle_islamic_studies" + ], + "arabicmmlu_social_science": [ + "arabicmmlu_high_civics", + "arabicmmlu_high_geography", + "arabicmmlu_high_economics", + "arabicmmlu_primary_social_science", + "arabicmmlu_univ_economics", + "arabicmmlu_primary_geography", + "arabicmmlu_middle_social_science", + "arabicmmlu_middle_economics", + "arabicmmlu_middle_geography", + "arabicmmlu_univ_accounting", + "arabicmmlu_middle_civics", + "arabicmmlu_univ_political_science" + ], + "arabicmmlu_other": [ + "arabicmmlu_univ_management", + "arabicmmlu_middle_general_knowledge", + "arabicmmlu_primary_general_knowledge", + "arabicmmlu_general_knowledge", + "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": true + }, + "arabicmmlu_middle_geography": { + "acc": true + }, + "arabicmmlu_middle_history": { + "acc": true + }, + "arabicmmlu_middle_islamic_studies": { + "acc": true + }, + "arabicmmlu_middle_natural_science": { + "acc": true + }, + "arabicmmlu_middle_social_science": { + "acc": true + }, + "arabicmmlu_other": { + "acc": true + }, + "arabicmmlu_primary_arabic_language": { + "acc": true + }, + "arabicmmlu_primary_computer_science": { + "acc": true + }, + "arabicmmlu_primary_general_knowledge": { + "acc": true + }, + "arabicmmlu_primary_geography": { + "acc": true + }, + "arabicmmlu_primary_history": { + "acc": true + }, + "arabicmmlu_primary_islamic_studies": { + "acc": true + }, + "arabicmmlu_primary_math": { + "acc": true + }, + "arabicmmlu_primary_natural_science": { + "acc": true + }, + "arabicmmlu_primary_social_science": { + "acc": true + }, + "arabicmmlu_prof_law": { + "acc": true + }, + "arabicmmlu_social_science": { + "acc": true + }, + "arabicmmlu_stem": { + "acc": true + }, + "arabicmmlu_univ_accounting": { + "acc": true + }, + "arabicmmlu_univ_computer_science": { + "acc": true + }, + "arabicmmlu_univ_economics": { + "acc": true + }, + "arabicmmlu_univ_management": { + "acc": true + }, + "arabicmmlu_univ_political_science": { + "acc": true + } + }, + "n-samples": { + "arabicmmlu_univ_management": { + "original": 75, + "effective": 75 + }, + "arabicmmlu_middle_general_knowledge": { + "original": 172, + "effective": 172 + }, + "arabicmmlu_primary_general_knowledge": { + "original": 162, + "effective": 162 + }, + "arabicmmlu_general_knowledge": { + "original": 864, + "effective": 864 + }, + "arabicmmlu_driving_test": { + "original": 1211, + "effective": 1211 + }, + "arabicmmlu_high_civics": { + "original": 87, + "effective": 87 + }, + "arabicmmlu_high_geography": { + "original": 1038, + "effective": 1038 + }, + "arabicmmlu_high_economics": { + "original": 360, + "effective": 360 + }, + "arabicmmlu_primary_social_science": { + "original": 705, + "effective": 705 + }, + "arabicmmlu_univ_economics": { + "original": 137, + "effective": 137 + }, + "arabicmmlu_primary_geography": { + "original": 57, + "effective": 57 + }, + "arabicmmlu_middle_social_science": { + "original": 241, + "effective": 241 + }, + "arabicmmlu_middle_economics": { + "original": 87, + "effective": 87 + }, + "arabicmmlu_middle_geography": { + "original": 272, + "effective": 272 + }, + "arabicmmlu_univ_accounting": { + "original": 74, + "effective": 74 + }, + "arabicmmlu_middle_civics": { + "original": 236, + "effective": 236 + }, + "arabicmmlu_univ_political_science": { + "original": 210, + "effective": 210 + }, + "arabicmmlu_prof_law": { + "original": 314, + "effective": 314 + }, + "arabicmmlu_middle_history": { + "original": 203, + "effective": 203 + }, + "arabicmmlu_primary_islamic_studies": { + "original": 999, + "effective": 999 + }, + "arabicmmlu_high_philosophy": { + "original": 39, + "effective": 39 + }, + "arabicmmlu_high_islamic_studies": { + "original": 334, + "effective": 334 + }, + "arabicmmlu_islamic_studies": { + "original": 639, + "effective": 639 + }, + "arabicmmlu_primary_history": { + "original": 102, + "effective": 102 + }, + "arabicmmlu_high_history": { + "original": 760, + "effective": 760 + }, + "arabicmmlu_middle_islamic_studies": { + "original": 238, + "effective": 238 + }, + "arabicmmlu_high_physics": { + "original": 255, + "effective": 255 + }, + "arabicmmlu_primary_math": { + "original": 409, + "effective": 409 + }, + "arabicmmlu_high_computer_science": { + "original": 261, + "effective": 261 + }, + "arabicmmlu_middle_natural_science": { + "original": 242, + "effective": 242 + }, + "arabicmmlu_high_biology": { + "original": 1409, + "effective": 1409 + }, + "arabicmmlu_primary_computer_science": { + "original": 190, + "effective": 190 + }, + "arabicmmlu_primary_natural_science": { + "original": 336, + "effective": 336 + }, + "arabicmmlu_univ_computer_science": { + "original": 64, + "effective": 64 + }, + "arabicmmlu_middle_computer_science": { + "original": 27, + "effective": 27 + }, + "arabicmmlu_primary_arabic_language": { + "original": 252, + "effective": 252 + }, + "arabicmmlu_middle_arabic_language": { + "original": 27, + "effective": 27 + }, + "arabicmmlu_high_arabic_language": { + "original": 390, + "effective": 390 + }, + "arabicmmlu_arabic_language_(grammar)": { + "original": 365, + "effective": 365 + }, + "arabicmmlu_arabic_language_(general)": { + "original": 612, + "effective": 612 + } + }, + "config": { + "model": "hf", + "model_args": "pretrained=FreedomIntelligence/AceGPT-v2-32B-Chat,trust_remote_code=True,cache_dir=/tmp,parallelize=False", + "model_num_parameters": 32512545792, + "model_dtype": "torch.float16", + "model_revision": "main", + "model_sha": "1c0ca4fb3fa4c292ac3d1f64f330f210c9f184d4", + "batch_size": "auto", + "batch_sizes": [ + 4 + ], + "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": "788a3672", + "date": 1737779092.1744986, + "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": "4.48.1", + "upper_git_hash": "086919bd66f4e15fdcd4b792a7b27a698c1ba091", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151643, + "max_length": 32768, + "task_hashes": {}, + "model_source": "hf", + "model_name": "FreedomIntelligence/AceGPT-v2-32B-Chat", + "model_name_sanitized": "FreedomIntelligence__AceGPT-v2-32B-Chat", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": null, + "chat_template_sha": null, + "start_time": 25942.251738535, + "end_time": 26447.764031496, + "total_evaluation_time_seconds": "505.51229296100064" +} \ No newline at end of file