evaluations/ar/Meta-Llama-3.1-8B-Instruct/openaimmlu_0_shot.json

{
  "results": {
    "openaimmlu": {
      "acc,none": 0.44666001994017945,
      "acc_stderr,none": 0.004112616445357971,
      "alias": "openaimmlu"
    },
    "openaimmlu_STEM": {
      "acc,none": 0.40794701986754967,
      "acc_stderr,none": 0.008874683686325746,
      "alias": " - STEM"
    },
    "openaimmlu_abstract_algebra": {
      "alias": "  - abstract_algebra",
      "acc,none": 0.3,
      "acc_stderr,none": 0.046056618647183814
    },
    "openaimmlu_astronomy": {
      "alias": "  - astronomy",
      "acc,none": 0.5328947368421053,
      "acc_stderr,none": 0.040601270352363966
    },
    "openaimmlu_college_biology": {
      "alias": "  - college_biology",
      "acc,none": 0.4583333333333333,
      "acc_stderr,none": 0.04166666666666665
    },
    "openaimmlu_college_chemistry": {
      "alias": "  - college_chemistry",
      "acc,none": 0.43,
      "acc_stderr,none": 0.04975698519562427
    },
    "openaimmlu_college_computer_science": {
      "alias": "  - college_computer_science",
      "acc,none": 0.35,
      "acc_stderr,none": 0.047937248544110196
    },
    "openaimmlu_college_mathematics": {
      "alias": "  - college_mathematics",
      "acc,none": 0.35,
      "acc_stderr,none": 0.0479372485441102
    },
    "openaimmlu_college_physics": {
      "alias": "  - college_physics",
      "acc,none": 0.35294117647058826,
      "acc_stderr,none": 0.04755129616062946
    },
    "openaimmlu_computer_security": {
      "alias": "  - computer_security",
      "acc,none": 0.44,
      "acc_stderr,none": 0.04988876515698589
    },
    "openaimmlu_conceptual_physics": {
      "alias": "  - conceptual_physics",
      "acc,none": 0.37446808510638296,
      "acc_stderr,none": 0.031639106653672915
    },
    "openaimmlu_econometrics": {
      "alias": "  - econometrics",
      "acc,none": 0.2807017543859649,
      "acc_stderr,none": 0.042270544512322
    },
    "openaimmlu_electrical_engineering": {
      "alias": "  - electrical_engineering",
      "acc,none": 0.4413793103448276,
      "acc_stderr,none": 0.04137931034482758
    },
    "openaimmlu_elementary_mathematics": {
      "alias": "  - elementary_mathematics",
      "acc,none": 0.3783068783068783,
      "acc_stderr,none": 0.024976954053155243
    },
    "openaimmlu_high_school_biology": {
      "alias": "  - high_school_biology",
      "acc,none": 0.5419354838709678,
      "acc_stderr,none": 0.028343787250540625
    },
    "openaimmlu_high_school_chemistry": {
      "alias": "  - high_school_chemistry",
      "acc,none": 0.41379310344827586,
      "acc_stderr,none": 0.03465304488406796
    },
    "openaimmlu_high_school_computer_science": {
      "alias": "  - high_school_computer_science",
      "acc,none": 0.5,
      "acc_stderr,none": 0.050251890762960605
    },
    "openaimmlu_high_school_mathematics": {
      "alias": "  - high_school_mathematics",
      "acc,none": 0.35555555555555557,
      "acc_stderr,none": 0.0291857149498574
    },
    "openaimmlu_high_school_physics": {
      "alias": "  - high_school_physics",
      "acc,none": 0.3509933774834437,
      "acc_stderr,none": 0.038969819642573754
    },
    "openaimmlu_high_school_statistics": {
      "alias": "  - high_school_statistics",
      "acc,none": 0.3888888888888889,
      "acc_stderr,none": 0.03324708911809117
    },
    "openaimmlu_humanities": {
      "acc,none": 0.5144124168514412,
      "acc_stderr,none": 0.011703005860087082,
      "alias": " - Humanities"
    },
    "openaimmlu_high_school_european_history": {
      "alias": "  - high_school_european_history",
      "acc,none": 0.5696969696969697,
      "acc_stderr,none": 0.03866225962879077
    },
    "openaimmlu_high_school_us_history": {
      "alias": "  - high_school_us_history",
      "acc,none": 0.5245098039215687,
      "acc_stderr,none": 0.035050931943487976
    },
    "openaimmlu_high_school_world_history": {
      "alias": "  - high_school_world_history",
      "acc,none": 0.5991561181434599,
      "acc_stderr,none": 0.031900803894732356
    },
    "openaimmlu_international_law": {
      "alias": "  - international_law",
      "acc,none": 0.6115702479338843,
      "acc_stderr,none": 0.044492703500683836
    },
    "openaimmlu_jurisprudence": {
      "alias": "  - jurisprudence",
      "acc,none": 0.5555555555555556,
      "acc_stderr,none": 0.04803752235190192
    },
    "openaimmlu_logical_fallacies": {
      "alias": "  - logical_fallacies",
      "acc,none": 0.4723926380368098,
      "acc_stderr,none": 0.0392237829061099
    },
    "openaimmlu_philosophy": {
      "alias": "  - philosophy",
      "acc,none": 0.47266881028938906,
      "acc_stderr,none": 0.02835563356832818
    },
    "openaimmlu_prehistory": {
      "alias": "  - prehistory",
      "acc,none": 0.4228395061728395,
      "acc_stderr,none": 0.027487472980871598
    },
    "openaimmlu_world_religions": {
      "alias": "  - world_religions",
      "acc,none": 0.5263157894736842,
      "acc_stderr,none": 0.038295098689947286
    },
    "openaimmlu_other": {
      "acc,none": 0.4364463924477411,
      "acc_stderr,none": 0.00633626561036892,
      "alias": " - Other"
    },
    "openaimmlu_anatomy": {
      "alias": "  - anatomy",
      "acc,none": 0.37037037037037035,
      "acc_stderr,none": 0.04171654161354544
    },
    "openaimmlu_clinical_knowledge": {
      "alias": "  - clinical_knowledge",
      "acc,none": 0.5056603773584906,
      "acc_stderr,none": 0.03077090076385131
    },
    "openaimmlu_college_medicine": {
      "alias": "  - college_medicine",
      "acc,none": 0.4508670520231214,
      "acc_stderr,none": 0.03794012674697029
    },
    "openaimmlu_formal_logic": {
      "alias": "  - formal_logic",
      "acc,none": 0.3333333333333333,
      "acc_stderr,none": 0.04216370213557835
    },
    "openaimmlu_global_facts": {
      "alias": "  - global_facts",
      "acc,none": 0.34,
      "acc_stderr,none": 0.04760952285695235
    },
    "openaimmlu_high_school_geography": {
      "alias": "  - high_school_geography",
      "acc,none": 0.5858585858585859,
      "acc_stderr,none": 0.035094383488796295
    },
    "openaimmlu_high_school_psychology": {
      "alias": "  - high_school_psychology",
      "acc,none": 0.5431192660550459,
      "acc_stderr,none": 0.021357458785226203
    },
    "openaimmlu_human_aging": {
      "alias": "  - human_aging",
      "acc,none": 0.47533632286995514,
      "acc_stderr,none": 0.03351695167652628
    },
    "openaimmlu_machine_learning": {
      "alias": "  - machine_learning",
      "acc,none": 0.25,
      "acc_stderr,none": 0.04109974682633932
    },
    "openaimmlu_medical_genetics": {
      "alias": "  - medical_genetics",
      "acc,none": 0.56,
      "acc_stderr,none": 0.04988876515698589
    },
    "openaimmlu_miscellaneous": {
      "alias": "  - miscellaneous",
      "acc,none": 0.5440613026819924,
      "acc_stderr,none": 0.01781040392543535
    },
    "openaimmlu_nutrition": {
      "alias": "  - nutrition",
      "acc,none": 0.5294117647058824,
      "acc_stderr,none": 0.028580341065138286
    },
    "openaimmlu_professional_accounting": {
      "alias": "  - professional_accounting",
      "acc,none": 0.3475177304964539,
      "acc_stderr,none": 0.028406627809590947
    },
    "openaimmlu_professional_law": {
      "alias": "  - professional_law",
      "acc,none": 0.3396349413298566,
      "acc_stderr,none": 0.01209559250693197
    },
    "openaimmlu_professional_medicine": {
      "alias": "  - professional_medicine",
      "acc,none": 0.47794117647058826,
      "acc_stderr,none": 0.030343264224213528
    },
    "openaimmlu_professional_psychology": {
      "alias": "  - professional_psychology",
      "acc,none": 0.4035947712418301,
      "acc_stderr,none": 0.019848280168401164
    },
    "openaimmlu_virology": {
      "alias": "  - virology",
      "acc,none": 0.39156626506024095,
      "acc_stderr,none": 0.03799857454479637
    },
    "openaimmlu_social_science": {
      "acc,none": 0.46348143639683503,
      "acc_stderr,none": 0.008379584468677955,
      "alias": " - Social Science"
    },
    "openaimmlu_business_ethics": {
      "alias": "  - business_ethics",
      "acc,none": 0.54,
      "acc_stderr,none": 0.05009082659620332
    },
    "openaimmlu_high_school_government_and_politics": {
      "alias": "  - high_school_government_and_politics",
      "acc,none": 0.5440414507772021,
      "acc_stderr,none": 0.035944137112724366
    },
    "openaimmlu_high_school_macroeconomics": {
      "alias": "  - high_school_macroeconomics",
      "acc,none": 0.46923076923076923,
      "acc_stderr,none": 0.025302958890850154
    },
    "openaimmlu_high_school_microeconomics": {
      "alias": "  - high_school_microeconomics",
      "acc,none": 0.5252100840336135,
      "acc_stderr,none": 0.03243718055137411
    },
    "openaimmlu_human_sexuality": {
      "alias": "  - human_sexuality",
      "acc,none": 0.5267175572519084,
      "acc_stderr,none": 0.04379024936553894
    },
    "openaimmlu_management": {
      "alias": "  - management",
      "acc,none": 0.5631067961165048,
      "acc_stderr,none": 0.04911147107365777
    },
    "openaimmlu_marketing": {
      "alias": "  - marketing",
      "acc,none": 0.6324786324786325,
      "acc_stderr,none": 0.03158539157745636
    },
    "openaimmlu_moral_disputes": {
      "alias": "  - moral_disputes",
      "acc,none": 0.47109826589595377,
      "acc_stderr,none": 0.02687408588351835
    },
    "openaimmlu_moral_scenarios": {
      "alias": "  - moral_scenarios",
      "acc,none": 0.2569832402234637,
      "acc_stderr,none": 0.014614465821966342
    },
    "openaimmlu_public_relations": {
      "alias": "  - public_relations",
      "acc,none": 0.4818181818181818,
      "acc_stderr,none": 0.04785964010794916
    },
    "openaimmlu_security_studies": {
      "alias": "  - security_studies",
      "acc,none": 0.5836734693877551,
      "acc_stderr,none": 0.03155782816556164
    },
    "openaimmlu_sociology": {
      "alias": "  - sociology",
      "acc,none": 0.6318407960199005,
      "acc_stderr,none": 0.03410410565495302
    },
    "openaimmlu_us_foreign_policy": {
      "alias": "  - us_foreign_policy",
      "acc,none": 0.65,
      "acc_stderr,none": 0.047937248544110196
    }
  },
  "groups": {
    "openaimmlu": {
      "acc,none": 0.44666001994017945,
      "acc_stderr,none": 0.004112616445357971,
      "alias": "openaimmlu"
    },
    "openaimmlu_STEM": {
      "acc,none": 0.40794701986754967,
      "acc_stderr,none": 0.008874683686325746,
      "alias": " - STEM"
    },
    "openaimmlu_humanities": {
      "acc,none": 0.5144124168514412,
      "acc_stderr,none": 0.011703005860087082,
      "alias": " - Humanities"
    },
    "openaimmlu_other": {
      "acc,none": 0.4364463924477411,
      "acc_stderr,none": 0.00633626561036892,
      "alias": " - Other"
    },
    "openaimmlu_social_science": {
      "acc,none": 0.46348143639683503,
      "acc_stderr,none": 0.008379584468677955,
      "alias": " - Social Science"
    }
  },
  "group_subtasks": {
    "openaimmlu_humanities": [
      "openaimmlu_jurisprudence",
      "openaimmlu_prehistory",
      "openaimmlu_world_religions",
      "openaimmlu_high_school_european_history",
      "openaimmlu_logical_fallacies",
      "openaimmlu_international_law",
      "openaimmlu_high_school_us_history",
      "openaimmlu_high_school_world_history",
      "openaimmlu_philosophy"
    ],
    "openaimmlu_social_science": [
      "openaimmlu_high_school_government_and_politics",
      "openaimmlu_human_sexuality",
      "openaimmlu_high_school_microeconomics",
      "openaimmlu_security_studies",
      "openaimmlu_public_relations",
      "openaimmlu_moral_disputes",
      "openaimmlu_high_school_macroeconomics",
      "openaimmlu_sociology",
      "openaimmlu_marketing",
      "openaimmlu_management",
      "openaimmlu_business_ethics",
      "openaimmlu_us_foreign_policy",
      "openaimmlu_moral_scenarios"
    ],
    "openaimmlu_other": [
      "openaimmlu_nutrition",
      "openaimmlu_professional_law",
      "openaimmlu_clinical_knowledge",
      "openaimmlu_college_medicine",
      "openaimmlu_human_aging",
      "openaimmlu_miscellaneous",
      "openaimmlu_global_facts",
      "openaimmlu_professional_medicine",
      "openaimmlu_machine_learning",
      "openaimmlu_professional_accounting",
      "openaimmlu_high_school_psychology",
      "openaimmlu_medical_genetics",
      "openaimmlu_virology",
      "openaimmlu_high_school_geography",
      "openaimmlu_professional_psychology",
      "openaimmlu_formal_logic",
      "openaimmlu_anatomy"
    ],
    "openaimmlu_STEM": [
      "openaimmlu_high_school_mathematics",
      "openaimmlu_college_computer_science",
      "openaimmlu_college_chemistry",
      "openaimmlu_high_school_chemistry",
      "openaimmlu_econometrics",
      "openaimmlu_astronomy",
      "openaimmlu_college_physics",
      "openaimmlu_computer_security",
      "openaimmlu_high_school_statistics",
      "openaimmlu_high_school_physics",
      "openaimmlu_electrical_engineering",
      "openaimmlu_elementary_mathematics",
      "openaimmlu_high_school_computer_science",
      "openaimmlu_abstract_algebra",
      "openaimmlu_college_mathematics",
      "openaimmlu_conceptual_physics",
      "openaimmlu_high_school_biology",
      "openaimmlu_college_biology"
    ],
    "openaimmlu": [
      "openaimmlu_STEM",
      "openaimmlu_other",
      "openaimmlu_social_science",
      "openaimmlu_humanities"
    ]
  },
  "configs": {
    "openaimmlu_abstract_algebra": {
      "task": "openaimmlu_abstract_algebra",
      "task_alias": "abstract_algebra",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "abstract_algebra",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_anatomy": {
      "task": "openaimmlu_anatomy",
      "task_alias": "anatomy",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "anatomy",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_astronomy": {
      "task": "openaimmlu_astronomy",
      "task_alias": "astronomy",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "astronomy",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_business_ethics": {
      "task": "openaimmlu_business_ethics",
      "task_alias": "business_ethics",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "business_ethics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_clinical_knowledge": {
      "task": "openaimmlu_clinical_knowledge",
      "task_alias": "clinical_knowledge",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "clinical_knowledge",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_college_biology": {
      "task": "openaimmlu_college_biology",
      "task_alias": "college_biology",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "college_biology",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_college_chemistry": {
      "task": "openaimmlu_college_chemistry",
      "task_alias": "college_chemistry",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "college_chemistry",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_college_computer_science": {
      "task": "openaimmlu_college_computer_science",
      "task_alias": "college_computer_science",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "college_computer_science",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_college_mathematics": {
      "task": "openaimmlu_college_mathematics",
      "task_alias": "college_mathematics",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "college_mathematics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_college_medicine": {
      "task": "openaimmlu_college_medicine",
      "task_alias": "college_medicine",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "college_medicine",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_college_physics": {
      "task": "openaimmlu_college_physics",
      "task_alias": "college_physics",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "college_physics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_computer_security": {
      "task": "openaimmlu_computer_security",
      "task_alias": "computer_security",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "computer_security",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_conceptual_physics": {
      "task": "openaimmlu_conceptual_physics",
      "task_alias": "conceptual_physics",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "conceptual_physics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_econometrics": {
      "task": "openaimmlu_econometrics",
      "task_alias": "econometrics",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "econometrics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_electrical_engineering": {
      "task": "openaimmlu_electrical_engineering",
      "task_alias": "electrical_engineering",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "electrical_engineering",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_elementary_mathematics": {
      "task": "openaimmlu_elementary_mathematics",
      "task_alias": "elementary_mathematics",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "elementary_mathematics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_formal_logic": {
      "task": "openaimmlu_formal_logic",
      "task_alias": "formal_logic",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "formal_logic",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_global_facts": {
      "task": "openaimmlu_global_facts",
      "task_alias": "global_facts",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "global_facts",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_biology": {
      "task": "openaimmlu_high_school_biology",
      "task_alias": "high_school_biology",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_biology",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_chemistry": {
      "task": "openaimmlu_high_school_chemistry",
      "task_alias": "high_school_chemistry",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_chemistry",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_computer_science": {
      "task": "openaimmlu_high_school_computer_science",
      "task_alias": "high_school_computer_science",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_computer_science",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_european_history": {
      "task": "openaimmlu_high_school_european_history",
      "task_alias": "high_school_european_history",
      "tag": "openaimmlu_humanities_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_european_history",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_geography": {
      "task": "openaimmlu_high_school_geography",
      "task_alias": "high_school_geography",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_geography",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_government_and_politics": {
      "task": "openaimmlu_high_school_government_and_politics",
      "task_alias": "high_school_government_and_politics",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_government_and_politics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_macroeconomics": {
      "task": "openaimmlu_high_school_macroeconomics",
      "task_alias": "high_school_macroeconomics",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_macroeconomics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_mathematics": {
      "task": "openaimmlu_high_school_mathematics",
      "task_alias": "high_school_mathematics",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_mathematics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_microeconomics": {
      "task": "openaimmlu_high_school_microeconomics",
      "task_alias": "high_school_microeconomics",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_microeconomics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_physics": {
      "task": "openaimmlu_high_school_physics",
      "task_alias": "high_school_physics",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_physics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_psychology": {
      "task": "openaimmlu_high_school_psychology",
      "task_alias": "high_school_psychology",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_psychology",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_statistics": {
      "task": "openaimmlu_high_school_statistics",
      "task_alias": "high_school_statistics",
      "tag": "openaimmlu_STEM_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_statistics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_us_history": {
      "task": "openaimmlu_high_school_us_history",
      "task_alias": "high_school_us_history",
      "tag": "openaimmlu_humanities_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_us_history",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_high_school_world_history": {
      "task": "openaimmlu_high_school_world_history",
      "task_alias": "high_school_world_history",
      "tag": "openaimmlu_humanities_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "high_school_world_history",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_human_aging": {
      "task": "openaimmlu_human_aging",
      "task_alias": "human_aging",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "human_aging",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_human_sexuality": {
      "task": "openaimmlu_human_sexuality",
      "task_alias": "human_sexuality",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "human_sexuality",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_international_law": {
      "task": "openaimmlu_international_law",
      "task_alias": "international_law",
      "tag": "openaimmlu_humanities_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "international_law",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_jurisprudence": {
      "task": "openaimmlu_jurisprudence",
      "task_alias": "jurisprudence",
      "tag": "openaimmlu_humanities_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "jurisprudence",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_logical_fallacies": {
      "task": "openaimmlu_logical_fallacies",
      "task_alias": "logical_fallacies",
      "tag": "openaimmlu_humanities_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "logical_fallacies",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_machine_learning": {
      "task": "openaimmlu_machine_learning",
      "task_alias": "machine_learning",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "machine_learning",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_management": {
      "task": "openaimmlu_management",
      "task_alias": "management",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "management",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_marketing": {
      "task": "openaimmlu_marketing",
      "task_alias": "marketing",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "marketing",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_medical_genetics": {
      "task": "openaimmlu_medical_genetics",
      "task_alias": "medical_genetics",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "medical_genetics",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_miscellaneous": {
      "task": "openaimmlu_miscellaneous",
      "task_alias": "miscellaneous",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "miscellaneous",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_moral_disputes": {
      "task": "openaimmlu_moral_disputes",
      "task_alias": "moral_disputes",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "moral_disputes",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_moral_scenarios": {
      "task": "openaimmlu_moral_scenarios",
      "task_alias": "moral_scenarios",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "moral_scenarios",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_nutrition": {
      "task": "openaimmlu_nutrition",
      "task_alias": "nutrition",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "nutrition",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_philosophy": {
      "task": "openaimmlu_philosophy",
      "task_alias": "philosophy",
      "tag": "openaimmlu_humanities_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "philosophy",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_prehistory": {
      "task": "openaimmlu_prehistory",
      "task_alias": "prehistory",
      "tag": "openaimmlu_humanities_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "prehistory",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_professional_accounting": {
      "task": "openaimmlu_professional_accounting",
      "task_alias": "professional_accounting",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "professional_accounting",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_professional_law": {
      "task": "openaimmlu_professional_law",
      "task_alias": "professional_law",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "professional_law",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_professional_medicine": {
      "task": "openaimmlu_professional_medicine",
      "task_alias": "professional_medicine",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "professional_medicine",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_professional_psychology": {
      "task": "openaimmlu_professional_psychology",
      "task_alias": "professional_psychology",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "professional_psychology",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_public_relations": {
      "task": "openaimmlu_public_relations",
      "task_alias": "public_relations",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "public_relations",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_security_studies": {
      "task": "openaimmlu_security_studies",
      "task_alias": "security_studies",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "security_studies",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_sociology": {
      "task": "openaimmlu_sociology",
      "task_alias": "sociology",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "sociology",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_us_foreign_policy": {
      "task": "openaimmlu_us_foreign_policy",
      "task_alias": "us_foreign_policy",
      "tag": "openaimmlu_social_science_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "us_foreign_policy",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_virology": {
      "task": "openaimmlu_virology",
      "task_alias": "virology",
      "tag": "openaimmlu_other_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "virology",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
      "doc_to_text": "query",
      "doc_to_target": "gold",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\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
      }
    },
    "openaimmlu_world_religions": {
      "task": "openaimmlu_world_religions",
      "task_alias": "world_religions",
      "tag": "openaimmlu_humanities_tasks",
      "dataset_path": "khalidalt/openai_mmlu_arabic",
      "dataset_name": "world_religions",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_docs(doc):\n\n        def format_example(doc, choices):\n            options = []\n            for _, choice in enumerate(choices):\n                options.append(f'{en2ar[choice]}. {doc[choice]}')\n\n            ar_subject = SUBJECTS[doc['Subject']]\n            query = PROMPT.format(ar_subject, #doc['Subject'],\n                                    doc['Question'],\n                                    \"\\n\".join(options))\n            return query\n\n        keys_en = [\"A\", \"B\", \"C\", \"D\"]\n        keys_ar = ['\u0623', '\u0628', '\u062c', '\u062f']\n        ar_label = en2ar[doc['Answer']]\n\n        out_doc = {\n            \"query\": format_example(doc, keys_en),\n            \"choices\": keys_ar,\n            \"gold\": keys_ar.index(ar_label)\n                }\n\n        return out_doc\n\n    return dataset.map(_process_docs)             \n",
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      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
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          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
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        "version": 0.0
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  "git_hash": "788a3672",
  "date": 1737779004.899056,
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  "tokenizer_eos_token": [
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