{ "results": { "arabicmmlu": { "acc,none": 0.6154271878242823, "acc_stderr,none": 0.003934302947200145, "alias": "arabicmmlu" }, "arabicmmlu_humanities": { "acc,none": 0.6063947078280044, "acc_stderr,none": 0.007795174544734088, "alias": " - Humanities" }, "arabicmmlu_high_history": { "alias": " - High History", "acc,none": 0.44473684210526315, "acc_stderr,none": 0.01803765580252778 }, "arabicmmlu_high_islamic_studies": { "alias": " - High Islamic Studies", "acc,none": 0.6616766467065869, "acc_stderr,none": 0.02592786608977119 }, "arabicmmlu_high_philosophy": { "alias": " - High Philosophy", "acc,none": 0.6153846153846154, "acc_stderr,none": 0.07892141169885801 }, "arabicmmlu_islamic_studies": { "alias": " - Islamic Studies", "acc,none": 0.43661971830985913, "acc_stderr,none": 0.019635508583285048 }, "arabicmmlu_middle_history": { "alias": " - Middle History", "acc,none": 0.6748768472906403, "acc_stderr,none": 0.032957975663112704 }, "arabicmmlu_middle_islamic_studies": { "alias": " - Middle Islamic Studies", "acc,none": 0.680672268907563, "acc_stderr,none": 0.0302839955258844 }, "arabicmmlu_primary_history": { "alias": " - Primary History", "acc,none": 0.5588235294117647, "acc_stderr,none": 0.04940635630605659 }, "arabicmmlu_primary_islamic_studies": { "alias": " - Primary Islamic Studies", "acc,none": 0.7497497497497497, "acc_stderr,none": 0.0137113480237793 }, "arabicmmlu_prof_law": { "alias": " - Prof Law", "acc,none": 0.7420382165605095, "acc_stderr,none": 0.024729688908190262 }, "arabicmmlu_language": { "acc,none": 0.6233292831105711, "acc_stderr,none": 0.011465056502784907, "alias": " - Language" }, "arabicmmlu_arabic_language_(general)": { "alias": " - Arabic Language (General)", "acc,none": 0.7320261437908496, "acc_stderr,none": 0.017917974069594722 }, "arabicmmlu_arabic_language_(grammar)": { "alias": " - Arabic Language (Grammar)", "acc,none": 0.6931506849315069, "acc_stderr,none": 0.02417273080537769 }, "arabicmmlu_high_arabic_language": { "alias": " - High Arabic Language", "acc,none": 0.38461538461538464, "acc_stderr,none": 0.024666744915187208 }, "arabicmmlu_middle_arabic_language": { "alias": " - Middle Arabic Language", "acc,none": 0.7037037037037037, "acc_stderr,none": 0.0895511888632576 }, "arabicmmlu_primary_arabic_language": { "alias": " - Primary Arabic Language", "acc,none": 0.6190476190476191, "acc_stderr,none": 0.030652119793011915 }, "arabicmmlu_other": { "acc,none": 0.643719806763285, "acc_stderr,none": 0.0095709414757183, "alias": " - Other" }, "arabicmmlu_driving_test": { "alias": " - Driving Test", "acc,none": 0.6688687035507844, "acc_stderr,none": 0.01352937914199443 }, "arabicmmlu_general_knowledge": { "alias": " - General Knowledge", "acc,none": 0.5810185185185185, "acc_stderr,none": 0.01679527052480067 }, "arabicmmlu_middle_general_knowledge": { "alias": " - Middle General Knowledge", "acc,none": 0.686046511627907, "acc_stderr,none": 0.03549043982227172 }, "arabicmmlu_primary_general_knowledge": { "alias": " - Primary General Knowledge", "acc,none": 0.7098765432098766, "acc_stderr,none": 0.035765960830111604 }, "arabicmmlu_univ_management": { "alias": " - Univ Management", "acc,none": 0.72, "acc_stderr,none": 0.052195060344100805 }, "arabicmmlu_social_science": { "acc,none": 0.6098744292237442, "acc_stderr,none": 0.00810834354787168, "alias": " - Social Science" }, "arabicmmlu_high_civics": { "alias": " - High Civics", "acc,none": 0.45977011494252873, "acc_stderr,none": 0.053741581963657706 }, "arabicmmlu_high_economics": { "alias": " - High Economics", "acc,none": 0.6527777777777778, "acc_stderr,none": 0.02512691742803579 }, "arabicmmlu_high_geography": { "alias": " - High Geography", "acc,none": 0.5144508670520231, "acc_stderr,none": 0.01552026616876521 }, "arabicmmlu_middle_civics": { "alias": " - Middle Civics", "acc,none": 0.5466101694915254, "acc_stderr,none": 0.032474375633194844 }, "arabicmmlu_middle_economics": { "alias": " - Middle Economics", "acc,none": 0.7701149425287356, "acc_stderr,none": 0.04537158185250774 }, "arabicmmlu_middle_geography": { "alias": " - Middle Geography", "acc,none": 0.6764705882352942, "acc_stderr,none": 0.02841820861940675 }, "arabicmmlu_middle_social_science": { "alias": " - Middle Social Science", "acc,none": 0.5394190871369294, "acc_stderr,none": 0.03217440335948302 }, "arabicmmlu_primary_geography": { "alias": " - Primary Geography", "acc,none": 0.6666666666666666, "acc_stderr,none": 0.0629940788348712 }, "arabicmmlu_primary_social_science": { "alias": " - Primary Social Science", "acc,none": 0.7319148936170212, "acc_stderr,none": 0.01669476485201052 }, "arabicmmlu_univ_accounting": { "alias": " - Univ Accounting", "acc,none": 0.7162162162162162, "acc_stderr,none": 0.05276603149821337 }, "arabicmmlu_univ_economics": { "alias": " - Univ Economics", "acc,none": 0.5912408759124088, "acc_stderr,none": 0.042154748403487034 }, "arabicmmlu_univ_political_science": { "alias": " - Univ Political Science", "acc,none": 0.6190476190476191, "acc_stderr,none": 0.03359110046749989 }, "arabicmmlu_stem": { "acc,none": 0.6056999686814908, "acc_stderr,none": 0.008320757741917867, "alias": " - STEM" }, "arabicmmlu_high_biology": { "alias": " - High Biology", "acc,none": 0.4868701206529453, "acc_stderr,none": 0.013320449671536705 }, "arabicmmlu_high_computer_science": { "alias": " - High Computer Science", "acc,none": 0.6513409961685823, "acc_stderr,none": 0.029554116131305663 }, "arabicmmlu_high_physics": { "alias": " - High Physics", "acc,none": 0.4588235294117647, "acc_stderr,none": 0.031266224025969486 }, "arabicmmlu_middle_computer_science": { "alias": " - Middle Computer Science", "acc,none": 0.9259259259259259, "acc_stderr,none": 0.05136112928011382 }, "arabicmmlu_middle_natural_science": { "alias": " - Middle Natural Science", "acc,none": 0.7603305785123967, "acc_stderr,none": 0.027497867883503148 }, "arabicmmlu_primary_computer_science": { "alias": " - Primary Computer Science", "acc,none": 0.7368421052631579, "acc_stderr,none": 0.032030558918430804 }, "arabicmmlu_primary_math": { "alias": " - Primary Math", "acc,none": 0.7041564792176039, "acc_stderr,none": 0.022596206734926304 }, "arabicmmlu_primary_natural_science": { "alias": " - Primary Natural Science", "acc,none": 0.8214285714285714, "acc_stderr,none": 0.020925145443913138 }, "arabicmmlu_univ_computer_science": { "alias": " - Univ Computer Science", "acc,none": 0.75, "acc_stderr,none": 0.05455447255899809 } }, "groups": { "arabicmmlu": { "acc,none": 0.6154271878242823, "acc_stderr,none": 0.003934302947200145, "alias": "arabicmmlu" }, "arabicmmlu_humanities": { "acc,none": 0.6063947078280044, "acc_stderr,none": 0.007795174544734088, "alias": " - Humanities" }, "arabicmmlu_language": { "acc,none": 0.6233292831105711, "acc_stderr,none": 0.011465056502784907, "alias": " - Language" }, "arabicmmlu_other": { "acc,none": 0.643719806763285, "acc_stderr,none": 0.0095709414757183, "alias": " - Other" }, "arabicmmlu_social_science": { "acc,none": 0.6098744292237442, "acc_stderr,none": 0.00810834354787168, "alias": " - Social Science" }, "arabicmmlu_stem": { "acc,none": 0.6056999686814908, "acc_stderr,none": 0.008320757741917867, "alias": " - STEM" } }, "group_subtasks": { "arabicmmlu_language": [ "arabicmmlu_high_arabic_language", "arabicmmlu_arabic_language_(grammar)", "arabicmmlu_middle_arabic_language", "arabicmmlu_primary_arabic_language", "arabicmmlu_arabic_language_(general)" ], "arabicmmlu_stem": [ "arabicmmlu_middle_computer_science", "arabicmmlu_high_physics", "arabicmmlu_primary_computer_science", "arabicmmlu_high_computer_science", "arabicmmlu_primary_natural_science", "arabicmmlu_primary_math", "arabicmmlu_univ_computer_science", "arabicmmlu_middle_natural_science", "arabicmmlu_high_biology" ], "arabicmmlu_humanities": [ "arabicmmlu_middle_history", "arabicmmlu_prof_law", "arabicmmlu_high_islamic_studies", "arabicmmlu_high_history", "arabicmmlu_high_philosophy", "arabicmmlu_islamic_studies", "arabicmmlu_primary_history", "arabicmmlu_primary_islamic_studies", "arabicmmlu_middle_islamic_studies" ], "arabicmmlu_social_science": [ "arabicmmlu_middle_civics", "arabicmmlu_univ_political_science", "arabicmmlu_high_geography", "arabicmmlu_middle_economics", "arabicmmlu_middle_geography", "arabicmmlu_high_civics", "arabicmmlu_univ_economics", "arabicmmlu_middle_social_science", "arabicmmlu_univ_accounting", "arabicmmlu_high_economics", "arabicmmlu_primary_geography", "arabicmmlu_primary_social_science" ], "arabicmmlu_other": [ "arabicmmlu_general_knowledge", "arabicmmlu_primary_general_knowledge", "arabicmmlu_middle_general_knowledge", "arabicmmlu_driving_test", "arabicmmlu_univ_management" ], "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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}, "arabicmmlu_middle_computer_science": { "original": 27, "effective": 27 }, "arabicmmlu_high_physics": { "original": 255, "effective": 255 }, "arabicmmlu_primary_computer_science": { "original": 190, "effective": 190 }, "arabicmmlu_high_computer_science": { "original": 261, "effective": 261 }, "arabicmmlu_primary_natural_science": { "original": 336, "effective": 336 }, "arabicmmlu_primary_math": { "original": 409, "effective": 409 }, "arabicmmlu_univ_computer_science": { "original": 64, "effective": 64 }, "arabicmmlu_middle_natural_science": { "original": 242, "effective": 242 }, "arabicmmlu_high_biology": { "original": 1409, "effective": 1409 }, "arabicmmlu_high_arabic_language": { "original": 390, "effective": 390 }, "arabicmmlu_arabic_language_(grammar)": { "original": 365, "effective": 365 }, "arabicmmlu_middle_arabic_language": { "original": 27, "effective": 27 }, "arabicmmlu_primary_arabic_language": { "original": 252, "effective": 252 }, "arabicmmlu_arabic_language_(general)": { "original": 612, "effective": 612 } }, "config": { "model": "hf", "model_args": "pretrained=Qwen/Qwen2.5-7B-Instruct,trust_remote_code=True,cache_dir=/tmp", "model_num_parameters": 7615616512, "model_dtype": "torch.bfloat16", "model_revision": "main", "model_sha": "bb46c15ee4bb56c5b63245ef50fd7637234d6f75", "batch_size": 1, "batch_sizes": [], "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": "8e1bd48d", "date": 1736532429.570835, "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 3.27.9\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [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: 12.3.107\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\nGPU 2: NVIDIA A100 80GB PCIe\nGPU 3: 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.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7\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): 96\nOn-line CPU(s) list: 0-95\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): 2\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: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (12 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\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==1.15.0rc2\n[pip3] open_clip_torch==2.26.1\n[pip3] optree==0.10.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.2.0a0\n[pip3] torchdata==0.7.0a0\n[pip3] torchdiffeq==0.2.4\n[pip3] torchmetrics==1.4.1\n[pip3] torchsde==0.2.6\n[pip3] torchtext==0.17.0a0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect", "transformers_version": "4.44.0", "upper_git_hash": null, "tokenizer_pad_token": [ "<|endoftext|>", "151643" ], "tokenizer_eos_token": [ "<|im_end|>", "151645" ], "tokenizer_bos_token": [ null, "None" ], "eot_token_id": 151645, "max_length": 32768, "task_hashes": {}, "model_source": "hf", "model_name": "Qwen/Qwen2.5-7B-Instruct", "model_name_sanitized": "Qwen__Qwen2.5-7B-Instruct", "system_instruction": null, "system_instruction_sha": null, "fewshot_as_multiturn": false, "chat_template": null, "chat_template_sha": null, "start_time": 388723.796268486, "end_time": 388932.518572279, "total_evaluation_time_seconds": "208.7223037930089" }