evaluations/ar/jais-family-13b-chat/arabicmmlu_0_shot.json

{
  "results": {
    "arabicmmlu": {
      "acc,none": 0.5813905223106192,
      "acc_stderr,none": 0.003974457419363176,
      "alias": "arabicmmlu"
    },
    "arabicmmlu_humanities": {
      "acc,none": 0.6207276736493936,
      "acc_stderr,none": 0.007676866448419673,
      "alias": " - Humanities"
    },
    "arabicmmlu_high_history": {
      "alias": "  - High History",
      "acc,none": 0.4605263157894737,
      "acc_stderr,none": 0.01809220376192219
    },
    "arabicmmlu_high_islamic_studies": {
      "alias": "  - High Islamic Studies",
      "acc,none": 0.6167664670658682,
      "acc_stderr,none": 0.026642195538092498
    },
    "arabicmmlu_high_philosophy": {
      "alias": "  - High Philosophy",
      "acc,none": 0.6666666666666666,
      "acc_stderr,none": 0.07647191129018725
    },
    "arabicmmlu_islamic_studies": {
      "alias": "  - Islamic Studies",
      "acc,none": 0.6071987480438185,
      "acc_stderr,none": 0.01933488200369804
    },
    "arabicmmlu_middle_history": {
      "alias": "  - Middle History",
      "acc,none": 0.6650246305418719,
      "acc_stderr,none": 0.033208527423483104
    },
    "arabicmmlu_middle_islamic_studies": {
      "alias": "  - Middle Islamic Studies",
      "acc,none": 0.6428571428571429,
      "acc_stderr,none": 0.031124619309328177
    },
    "arabicmmlu_primary_history": {
      "alias": "  - Primary History",
      "acc,none": 0.6862745098039216,
      "acc_stderr,none": 0.04617034827006718
    },
    "arabicmmlu_primary_islamic_studies": {
      "alias": "  - Primary Islamic Studies",
      "acc,none": 0.8138138138138138,
      "acc_stderr,none": 0.012321710081733966
    },
    "arabicmmlu_prof_law": {
      "alias": "  - Prof Law",
      "acc,none": 0.3535031847133758,
      "acc_stderr,none": 0.027021390361997532
    },
    "arabicmmlu_language": {
      "acc,none": 0.5595382746051033,
      "acc_stderr,none": 0.011907567989279312,
      "alias": " - Language"
    },
    "arabicmmlu_arabic_language_(general)": {
      "alias": "  - Arabic Language (General)",
      "acc,none": 0.6748366013071896,
      "acc_stderr,none": 0.018950886770806315
    },
    "arabicmmlu_arabic_language_(grammar)": {
      "alias": "  - Arabic Language (Grammar)",
      "acc,none": 0.5287671232876713,
      "acc_stderr,none": 0.02616370969480108
    },
    "arabicmmlu_high_arabic_language": {
      "alias": "  - High Arabic Language",
      "acc,none": 0.37435897435897436,
      "acc_stderr,none": 0.024537591572830496
    },
    "arabicmmlu_middle_arabic_language": {
      "alias": "  - Middle Arabic Language",
      "acc,none": 0.5185185185185185,
      "acc_stderr,none": 0.09799078929868857
    },
    "arabicmmlu_primary_arabic_language": {
      "alias": "  - Primary Arabic Language",
      "acc,none": 0.6150793650793651,
      "acc_stderr,none": 0.03071243955075999
    },
    "arabicmmlu_other": {
      "acc,none": 0.645330112721417,
      "acc_stderr,none": 0.009605570074720063,
      "alias": " - Other"
    },
    "arabicmmlu_driving_test": {
      "alias": "  - Driving Test",
      "acc,none": 0.6457473162675474,
      "acc_stderr,none": 0.013749762426221467
    },
    "arabicmmlu_general_knowledge": {
      "alias": "  - General Knowledge",
      "acc,none": 0.6516203703703703,
      "acc_stderr,none": 0.01621878455756233
    },
    "arabicmmlu_middle_general_knowledge": {
      "alias": "  - Middle General Knowledge",
      "acc,none": 0.6162790697674418,
      "acc_stderr,none": 0.03718762118238795
    },
    "arabicmmlu_primary_general_knowledge": {
      "alias": "  - Primary General Knowledge",
      "acc,none": 0.6604938271604939,
      "acc_stderr,none": 0.03732031330740126
    },
    "arabicmmlu_univ_management": {
      "alias": "  - Univ Management",
      "acc,none": 0.6,
      "acc_stderr,none": 0.05694947974514993
    },
    "arabicmmlu_social_science": {
      "acc,none": 0.560216894977169,
      "acc_stderr,none": 0.00821187595080662,
      "alias": " - Social Science"
    },
    "arabicmmlu_high_civics": {
      "alias": "  - High Civics",
      "acc,none": 0.4482758620689655,
      "acc_stderr,none": 0.053627116270410544
    },
    "arabicmmlu_high_economics": {
      "alias": "  - High Economics",
      "acc,none": 0.5916666666666667,
      "acc_stderr,none": 0.02594171859862409
    },
    "arabicmmlu_high_geography": {
      "alias": "  - High Geography",
      "acc,none": 0.4527938342967245,
      "acc_stderr,none": 0.015457397136918143
    },
    "arabicmmlu_middle_civics": {
      "alias": "  - Middle Civics",
      "acc,none": 0.4957627118644068,
      "acc_stderr,none": 0.032615232401979485
    },
    "arabicmmlu_middle_economics": {
      "alias": "  - Middle Economics",
      "acc,none": 0.7241379310344828,
      "acc_stderr,none": 0.04819560289115228
    },
    "arabicmmlu_middle_geography": {
      "alias": "  - Middle Geography",
      "acc,none": 0.6360294117647058,
      "acc_stderr,none": 0.029227192460032025
    },
    "arabicmmlu_middle_social_science": {
      "alias": "  - Middle Social Science",
      "acc,none": 0.4896265560165975,
      "acc_stderr,none": 0.0322679143822933
    },
    "arabicmmlu_primary_geography": {
      "alias": "  - Primary Geography",
      "acc,none": 0.7017543859649122,
      "acc_stderr,none": 0.061134390564663986
    },
    "arabicmmlu_primary_social_science": {
      "alias": "  - Primary Social Science",
      "acc,none": 0.7163120567375887,
      "acc_stderr,none": 0.01698968161579803
    },
    "arabicmmlu_univ_accounting": {
      "alias": "  - Univ Accounting",
      "acc,none": 0.5540540540540541,
      "acc_stderr,none": 0.058177592923397636
    },
    "arabicmmlu_univ_economics": {
      "alias": "  - Univ Economics",
      "acc,none": 0.5401459854014599,
      "acc_stderr,none": 0.04273622067714666
    },
    "arabicmmlu_univ_political_science": {
      "alias": "  - Univ Political Science",
      "acc,none": 0.5238095238095238,
      "acc_stderr,none": 0.034546488100476766
    },
    "arabicmmlu_stem": {
      "acc,none": 0.5214531788286878,
      "acc_stderr,none": 0.008539561905594092,
      "alias": " - STEM"
    },
    "arabicmmlu_high_biology": {
      "alias": "  - High Biology",
      "acc,none": 0.42086586231369766,
      "acc_stderr,none": 0.013157097879519403
    },
    "arabicmmlu_high_computer_science": {
      "alias": "  - High Computer Science",
      "acc,none": 0.5478927203065134,
      "acc_stderr,none": 0.030866105840801246
    },
    "arabicmmlu_high_physics": {
      "alias": "  - High Physics",
      "acc,none": 0.38823529411764707,
      "acc_stderr,none": 0.03057897034303606
    },
    "arabicmmlu_middle_computer_science": {
      "alias": "  - Middle Computer Science",
      "acc,none": 0.7777777777777778,
      "acc_stderr,none": 0.08153326507837146
    },
    "arabicmmlu_middle_natural_science": {
      "alias": "  - Middle Natural Science",
      "acc,none": 0.6735537190082644,
      "acc_stderr,none": 0.030205321356519606
    },
    "arabicmmlu_primary_computer_science": {
      "alias": "  - Primary Computer Science",
      "acc,none": 0.6894736842105263,
      "acc_stderr,none": 0.03365713545671698
    },
    "arabicmmlu_primary_math": {
      "alias": "  - Primary Math",
      "acc,none": 0.5134474327628362,
      "acc_stderr,none": 0.024744734365196468
    },
    "arabicmmlu_primary_natural_science": {
      "alias": "  - Primary Natural Science",
      "acc,none": 0.7767857142857143,
      "acc_stderr,none": 0.022750408778833355
    },
    "arabicmmlu_univ_computer_science": {
      "alias": "  - Univ Computer Science",
      "acc,none": 0.6875,
      "acc_stderr,none": 0.058397074018894594
    }
  },
  "groups": {
    "arabicmmlu": {
      "acc,none": 0.5813905223106192,
      "acc_stderr,none": 0.003974457419363176,
      "alias": "arabicmmlu"
    },
    "arabicmmlu_humanities": {
      "acc,none": 0.6207276736493936,
      "acc_stderr,none": 0.007676866448419673,
      "alias": " - Humanities"
    },
    "arabicmmlu_language": {
      "acc,none": 0.5595382746051033,
      "acc_stderr,none": 0.011907567989279312,
      "alias": " - Language"
    },
    "arabicmmlu_other": {
      "acc,none": 0.645330112721417,
      "acc_stderr,none": 0.009605570074720063,
      "alias": " - Other"
    },
    "arabicmmlu_social_science": {
      "acc,none": 0.560216894977169,
      "acc_stderr,none": 0.00821187595080662,
      "alias": " - Social Science"
    },
    "arabicmmlu_stem": {
      "acc,none": 0.5214531788286878,
      "acc_stderr,none": 0.008539561905594092,
      "alias": " - STEM"
    }
  },
  "group_subtasks": {
    "arabicmmlu_language": [
      "arabicmmlu_arabic_language_(general)",
      "arabicmmlu_high_arabic_language",
      "arabicmmlu_arabic_language_(grammar)",
      "arabicmmlu_primary_arabic_language",
      "arabicmmlu_middle_arabic_language"
    ],
    "arabicmmlu_stem": [
      "arabicmmlu_high_biology",
      "arabicmmlu_primary_computer_science",
      "arabicmmlu_primary_math",
      "arabicmmlu_high_physics",
      "arabicmmlu_middle_computer_science",
      "arabicmmlu_high_computer_science",
      "arabicmmlu_univ_computer_science",
      "arabicmmlu_primary_natural_science",
      "arabicmmlu_middle_natural_science"
    ],
    "arabicmmlu_humanities": [
      "arabicmmlu_middle_islamic_studies",
      "arabicmmlu_islamic_studies",
      "arabicmmlu_prof_law",
      "arabicmmlu_high_history",
      "arabicmmlu_primary_history",
      "arabicmmlu_high_philosophy",
      "arabicmmlu_primary_islamic_studies",
      "arabicmmlu_middle_history",
      "arabicmmlu_high_islamic_studies"
    ],
    "arabicmmlu_social_science": [
      "arabicmmlu_primary_geography",
      "arabicmmlu_high_economics",
      "arabicmmlu_middle_social_science",
      "arabicmmlu_middle_economics",
      "arabicmmlu_high_geography",
      "arabicmmlu_primary_social_science",
      "arabicmmlu_high_civics",
      "arabicmmlu_univ_political_science",
      "arabicmmlu_middle_geography",
      "arabicmmlu_middle_civics",
      "arabicmmlu_univ_economics",
      "arabicmmlu_univ_accounting"
    ],
    "arabicmmlu_other": [
      "arabicmmlu_univ_management",
      "arabicmmlu_primary_general_knowledge",
      "arabicmmlu_middle_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_primary_general_knowledge": {
      "original": 162,
      "effective": 162
    },
    "arabicmmlu_middle_general_knowledge": {
      "original": 172,
      "effective": 172
    },
    "arabicmmlu_general_knowledge": {
      "original": 864,
      "effective": 864
    },
    "arabicmmlu_driving_test": {
      "original": 1211,
      "effective": 1211
    },
    "arabicmmlu_primary_geography": {
      "original": 57,
      "effective": 57
    },
    "arabicmmlu_high_economics": {
      "original": 360,
      "effective": 360
    },
    "arabicmmlu_middle_social_science": {
      "original": 241,
      "effective": 241
    },
    "arabicmmlu_middle_economics": {
      "original": 87,
      "effective": 87
    },
    "arabicmmlu_high_geography": {
      "original": 1038,
      "effective": 1038
    },
    "arabicmmlu_primary_social_science": {
      "original": 705,
      "effective": 705
    },
    "arabicmmlu_high_civics": {
      "original": 87,
      "effective": 87
    },
    "arabicmmlu_univ_political_science": {
      "original": 210,
      "effective": 210
    },
    "arabicmmlu_middle_geography": {
      "original": 272,
      "effective": 272
    },
    "arabicmmlu_middle_civics": {
      "original": 236,
      "effective": 236
    },
    "arabicmmlu_univ_economics": {
      "original": 137,
      "effective": 137
    },
    "arabicmmlu_univ_accounting": {
      "original": 74,
      "effective": 74
    },
    "arabicmmlu_middle_islamic_studies": {
      "original": 238,
      "effective": 238
    },
    "arabicmmlu_islamic_studies": {
      "original": 639,
      "effective": 639
    },
    "arabicmmlu_prof_law": {
      "original": 314,
      "effective": 314
    },
    "arabicmmlu_high_history": {
      "original": 760,
      "effective": 760
    },
    "arabicmmlu_primary_history": {
      "original": 102,
      "effective": 102
    },
    "arabicmmlu_high_philosophy": {
      "original": 39,
      "effective": 39
    },
    "arabicmmlu_primary_islamic_studies": {
      "original": 999,
      "effective": 999
    },
    "arabicmmlu_middle_history": {
      "original": 203,
      "effective": 203
    },
    "arabicmmlu_high_islamic_studies": {
      "original": 334,
      "effective": 334
    },
    "arabicmmlu_high_biology": {
      "original": 1409,
      "effective": 1409
    },
    "arabicmmlu_primary_computer_science": {
      "original": 190,
      "effective": 190
    },
    "arabicmmlu_primary_math": {
      "original": 409,
      "effective": 409
    },
    "arabicmmlu_high_physics": {
      "original": 255,
      "effective": 255
    },
    "arabicmmlu_middle_computer_science": {
      "original": 27,
      "effective": 27
    },
    "arabicmmlu_high_computer_science": {
      "original": 261,
      "effective": 261
    },
    "arabicmmlu_univ_computer_science": {
      "original": 64,
      "effective": 64
    },
    "arabicmmlu_primary_natural_science": {
      "original": 336,
      "effective": 336
    },
    "arabicmmlu_middle_natural_science": {
      "original": 242,
      "effective": 242
    },
    "arabicmmlu_arabic_language_(general)": {
      "original": 612,
      "effective": 612
    },
    "arabicmmlu_high_arabic_language": {
      "original": 390,
      "effective": 390
    },
    "arabicmmlu_arabic_language_(grammar)": {
      "original": 365,
      "effective": 365
    },
    "arabicmmlu_primary_arabic_language": {
      "original": 252,
      "effective": 252
    },
    "arabicmmlu_middle_arabic_language": {
      "original": 27,
      "effective": 27
    }
  },
  "config": {
    "model": "vllm",
    "model_args": "pretrained=inceptionai/jais-family-13b-chat,tensor_parallel_size=1,data_parallel_size=2,gpu_memory_utilization=0.4,download_dir=/tmp",
    "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": 1735755943.4155445,
  "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": "4.47.1",
  "upper_git_hash": "f64fe2f2a86055aaecced603b56097fd79201711",
  "tokenizer_pad_token": [
    "<|endoftext|>",
    "0"
  ],
  "tokenizer_eos_token": [
    "<|endoftext|>",
    "0"
  ],
  "tokenizer_bos_token": [
    "<|endoftext|>",
    "0"
  ],
  "eot_token_id": 0,
  "max_length": 2048,
  "task_hashes": {},
  "model_source": "vllm",
  "model_name": "inceptionai/jais-family-13b-chat",
  "model_name_sanitized": "inceptionai__jais-family-13b-chat",
  "system_instruction": null,
  "system_instruction_sha": null,
  "fewshot_as_multiturn": false,
  "chat_template": null,
  "chat_template_sha": null,
  "start_time": 9749.039771719,
  "end_time": 10388.251187622,
  "total_evaluation_time_seconds": "639.2114159029989"
}