ALLaM-7B-Instruct-preview/evaluations/ar/Qwen2.5-14B-Instruct/openaimmlu_0_shot.json

{
  "results": {
    "openaimmlu": {
      " ": " ",
      "alias": "openaimmlu"
    },
    "openaimmlu_STEM": {
      "acc,none": 0.6125827814569537,
      "acc_stderr,none": 0.008598613803694075,
      "alias": " - STEM"
    },
    "openaimmlu_abstract_algebra": {
      "alias": "  - abstract_algebra",
      "acc,none": 0.39,
      "acc_stderr,none": 0.04902071300001975
    },
    "openaimmlu_astronomy": {
      "alias": "  - astronomy",
      "acc,none": 0.756578947368421,
      "acc_stderr,none": 0.034923496688842384
    },
    "openaimmlu_college_biology": {
      "alias": "  - college_biology",
      "acc,none": 0.7013888888888888,
      "acc_stderr,none": 0.03827052357950756
    },
    "openaimmlu_college_chemistry": {
      "alias": "  - college_chemistry",
      "acc,none": 0.41,
      "acc_stderr,none": 0.049431107042371025
    },
    "openaimmlu_college_computer_science": {
      "alias": "  - college_computer_science",
      "acc,none": 0.52,
      "acc_stderr,none": 0.050211673156867795
    },
    "openaimmlu_college_mathematics": {
      "alias": "  - college_mathematics",
      "acc,none": 0.44,
      "acc_stderr,none": 0.04988876515698589
    },
    "openaimmlu_college_physics": {
      "alias": "  - college_physics",
      "acc,none": 0.4411764705882353,
      "acc_stderr,none": 0.04940635630605659
    },
    "openaimmlu_computer_security": {
      "alias": "  - computer_security",
      "acc,none": 0.62,
      "acc_stderr,none": 0.048783173121456316
    },
    "openaimmlu_conceptual_physics": {
      "alias": "  - conceptual_physics",
      "acc,none": 0.6936170212765957,
      "acc_stderr,none": 0.030135906478517563
    },
    "openaimmlu_econometrics": {
      "alias": "  - econometrics",
      "acc,none": 0.49122807017543857,
      "acc_stderr,none": 0.04702880432049615
    },
    "openaimmlu_electrical_engineering": {
      "alias": "  - electrical_engineering",
      "acc,none": 0.5241379310344828,
      "acc_stderr,none": 0.041618085035015295
    },
    "openaimmlu_elementary_mathematics": {
      "alias": "  - elementary_mathematics",
      "acc,none": 0.6904761904761905,
      "acc_stderr,none": 0.023809523809523864
    },
    "openaimmlu_high_school_biology": {
      "alias": "  - high_school_biology",
      "acc,none": 0.7677419354838709,
      "acc_stderr,none": 0.024022256130308235
    },
    "openaimmlu_high_school_chemistry": {
      "alias": "  - high_school_chemistry",
      "acc,none": 0.6009852216748769,
      "acc_stderr,none": 0.03445487686264715
    },
    "openaimmlu_high_school_computer_science": {
      "alias": "  - high_school_computer_science",
      "acc,none": 0.82,
      "acc_stderr,none": 0.038612291966536955
    },
    "openaimmlu_high_school_mathematics": {
      "alias": "  - high_school_mathematics",
      "acc,none": 0.4888888888888889,
      "acc_stderr,none": 0.03047800981961583
    },
    "openaimmlu_high_school_physics": {
      "alias": "  - high_school_physics",
      "acc,none": 0.48344370860927155,
      "acc_stderr,none": 0.040802441856289715
    },
    "openaimmlu_high_school_statistics": {
      "alias": "  - high_school_statistics",
      "acc,none": 0.6851851851851852,
      "acc_stderr,none": 0.03167468706828978
    },
    "openaimmlu_humanities": {
      "acc,none": 0.7123059866962306,
      "acc_stderr,none": 0.010563497467305187,
      "alias": " - Humanities"
    },
    "openaimmlu_high_school_european_history": {
      "alias": "  - high_school_european_history",
      "acc,none": 0.793939393939394,
      "acc_stderr,none": 0.03158415324047709
    },
    "openaimmlu_high_school_us_history": {
      "alias": "  - high_school_us_history",
      "acc,none": 0.7794117647058824,
      "acc_stderr,none": 0.02910225438967409
    },
    "openaimmlu_high_school_world_history": {
      "alias": "  - high_school_world_history",
      "acc,none": 0.7848101265822784,
      "acc_stderr,none": 0.02675082699467617
    },
    "openaimmlu_international_law": {
      "alias": "  - international_law",
      "acc,none": 0.7603305785123967,
      "acc_stderr,none": 0.03896878985070416
    },
    "openaimmlu_jurisprudence": {
      "alias": "  - jurisprudence",
      "acc,none": 0.7314814814814815,
      "acc_stderr,none": 0.042844679680521934
    },
    "openaimmlu_logical_fallacies": {
      "alias": "  - logical_fallacies",
      "acc,none": 0.7177914110429447,
      "acc_stderr,none": 0.03536117886664743
    },
    "openaimmlu_philosophy": {
      "alias": "  - philosophy",
      "acc,none": 0.639871382636656,
      "acc_stderr,none": 0.02726429759980402
    },
    "openaimmlu_prehistory": {
      "alias": "  - prehistory",
      "acc,none": 0.6141975308641975,
      "acc_stderr,none": 0.027085401226132143
    },
    "openaimmlu_world_religions": {
      "alias": "  - world_religions",
      "acc,none": 0.7192982456140351,
      "acc_stderr,none": 0.034462962170884265
    },
    "openaimmlu_other": {
      "acc,none": 0.6031692515171949,
      "acc_stderr,none": 0.00615858158492755,
      "alias": " - Other"
    },
    "openaimmlu_anatomy": {
      "alias": "  - anatomy",
      "acc,none": 0.48148148148148145,
      "acc_stderr,none": 0.043163785995113245
    },
    "openaimmlu_clinical_knowledge": {
      "alias": "  - clinical_knowledge",
      "acc,none": 0.6528301886792452,
      "acc_stderr,none": 0.029300101705549652
    },
    "openaimmlu_college_medicine": {
      "alias": "  - college_medicine",
      "acc,none": 0.6242774566473989,
      "acc_stderr,none": 0.036928207672648664
    },
    "openaimmlu_formal_logic": {
      "alias": "  - formal_logic",
      "acc,none": 0.5634920634920635,
      "acc_stderr,none": 0.04435932892851466
    },
    "openaimmlu_global_facts": {
      "alias": "  - global_facts",
      "acc,none": 0.5,
      "acc_stderr,none": 0.050251890762960605
    },
    "openaimmlu_high_school_geography": {
      "alias": "  - high_school_geography",
      "acc,none": 0.7424242424242424,
      "acc_stderr,none": 0.031156269519646847
    },
    "openaimmlu_high_school_psychology": {
      "alias": "  - high_school_psychology",
      "acc,none": 0.7889908256880734,
      "acc_stderr,none": 0.01749392240411265
    },
    "openaimmlu_human_aging": {
      "alias": "  - human_aging",
      "acc,none": 0.6502242152466368,
      "acc_stderr,none": 0.03200736719484503
    },
    "openaimmlu_machine_learning": {
      "alias": "  - machine_learning",
      "acc,none": 0.5267857142857143,
      "acc_stderr,none": 0.04738975119274155
    },
    "openaimmlu_medical_genetics": {
      "alias": "  - medical_genetics",
      "acc,none": 0.67,
      "acc_stderr,none": 0.04725815626252609
    },
    "openaimmlu_miscellaneous": {
      "alias": "  - miscellaneous",
      "acc,none": 0.7509578544061303,
      "acc_stderr,none": 0.015464676163395976
    },
    "openaimmlu_nutrition": {
      "alias": "  - nutrition",
      "acc,none": 0.6862745098039216,
      "acc_stderr,none": 0.026568921015457155
    },
    "openaimmlu_professional_accounting": {
      "alias": "  - professional_accounting",
      "acc,none": 0.4716312056737589,
      "acc_stderr,none": 0.029779450957303055
    },
    "openaimmlu_professional_law": {
      "alias": "  - professional_law",
      "acc,none": 0.455019556714472,
      "acc_stderr,none": 0.012718456618701773
    },
    "openaimmlu_professional_medicine": {
      "alias": "  - professional_medicine",
      "acc,none": 0.6433823529411765,
      "acc_stderr,none": 0.02909720956841196
    },
    "openaimmlu_professional_psychology": {
      "alias": "  - professional_psychology",
      "acc,none": 0.619281045751634,
      "acc_stderr,none": 0.01964380155792481
    },
    "openaimmlu_virology": {
      "alias": "  - virology",
      "acc,none": 0.4819277108433735,
      "acc_stderr,none": 0.038899512528272166
    },
    "openaimmlu_social_science": {
      "acc,none": 0.6835057821059038,
      "acc_stderr,none": 0.007900267253552388,
      "alias": " - Social Science"
    },
    "openaimmlu_business_ethics": {
      "alias": "  - business_ethics",
      "acc,none": 0.73,
      "acc_stderr,none": 0.044619604333847394
    },
    "openaimmlu_high_school_government_and_politics": {
      "alias": "  - high_school_government_and_politics",
      "acc,none": 0.8497409326424871,
      "acc_stderr,none": 0.025787723180723882
    },
    "openaimmlu_high_school_macroeconomics": {
      "alias": "  - high_school_macroeconomics",
      "acc,none": 0.7384615384615385,
      "acc_stderr,none": 0.0222821412042044
    },
    "openaimmlu_high_school_microeconomics": {
      "alias": "  - high_school_microeconomics",
      "acc,none": 0.7941176470588235,
      "acc_stderr,none": 0.02626502460827588
    },
    "openaimmlu_human_sexuality": {
      "alias": "  - human_sexuality",
      "acc,none": 0.7175572519083969,
      "acc_stderr,none": 0.03948406125768362
    },
    "openaimmlu_management": {
      "alias": "  - management",
      "acc,none": 0.6990291262135923,
      "acc_stderr,none": 0.04541609446503948
    },
    "openaimmlu_marketing": {
      "alias": "  - marketing",
      "acc,none": 0.782051282051282,
      "acc_stderr,none": 0.027046857630716677
    },
    "openaimmlu_moral_disputes": {
      "alias": "  - moral_disputes",
      "acc,none": 0.6271676300578035,
      "acc_stderr,none": 0.02603389061357627
    },
    "openaimmlu_moral_scenarios": {
      "alias": "  - moral_scenarios",
      "acc,none": 0.5251396648044693,
      "acc_stderr,none": 0.01670135084268263
    },
    "openaimmlu_public_relations": {
      "alias": "  - public_relations",
      "acc,none": 0.7090909090909091,
      "acc_stderr,none": 0.04350271442923243
    },
    "openaimmlu_security_studies": {
      "alias": "  - security_studies",
      "acc,none": 0.7673469387755102,
      "acc_stderr,none": 0.02704925791589618
    },
    "openaimmlu_sociology": {
      "alias": "  - sociology",
      "acc,none": 0.746268656716418,
      "acc_stderr,none": 0.030769444967296024
    },
    "openaimmlu_us_foreign_policy": {
      "alias": "  - us_foreign_policy",
      "acc,none": 0.8,
      "acc_stderr,none": 0.04020151261036846
    }
  },
  "groups": {
    "openaimmlu_STEM": {
      "acc,none": 0.6125827814569537,
      "acc_stderr,none": 0.008598613803694075,
      "alias": " - STEM"
    },
    "openaimmlu_humanities": {
      "acc,none": 0.7123059866962306,
      "acc_stderr,none": 0.010563497467305187,
      "alias": " - Humanities"
    },
    "openaimmlu_other": {
      "acc,none": 0.6031692515171949,
      "acc_stderr,none": 0.00615858158492755,
      "alias": " - Other"
    },
    "openaimmlu_social_science": {
      "acc,none": 0.6835057821059038,
      "acc_stderr,none": 0.007900267253552388,
      "alias": " - Social Science"
    }
  },
  "group_subtasks": {
    "openaimmlu_humanities": [
      "openaimmlu_jurisprudence",
      "openaimmlu_logical_fallacies",
      "openaimmlu_prehistory",
      "openaimmlu_high_school_european_history",
      "openaimmlu_high_school_world_history",
      "openaimmlu_philosophy",
      "openaimmlu_high_school_us_history",
      "openaimmlu_world_religions",
      "openaimmlu_international_law"
    ],
    "openaimmlu_social_science": [
      "openaimmlu_human_sexuality",
      "openaimmlu_moral_disputes",
      "openaimmlu_moral_scenarios",
      "openaimmlu_high_school_microeconomics",
      "openaimmlu_business_ethics",
      "openaimmlu_sociology",
      "openaimmlu_high_school_government_and_politics",
      "openaimmlu_high_school_macroeconomics",
      "openaimmlu_marketing",
      "openaimmlu_public_relations",
      "openaimmlu_security_studies",
      "openaimmlu_management",
      "openaimmlu_us_foreign_policy"
    ],
    "openaimmlu_other": [
      "openaimmlu_clinical_knowledge",
      "openaimmlu_high_school_psychology",
      "openaimmlu_professional_law",
      "openaimmlu_machine_learning",
      "openaimmlu_human_aging",
      "openaimmlu_virology",
      "openaimmlu_miscellaneous",
      "openaimmlu_professional_medicine",
      "openaimmlu_anatomy",
      "openaimmlu_global_facts",
      "openaimmlu_professional_psychology",
      "openaimmlu_high_school_geography",
      "openaimmlu_medical_genetics",
      "openaimmlu_professional_accounting",
      "openaimmlu_formal_logic",
      "openaimmlu_college_medicine",
      "openaimmlu_nutrition"
    ],
    "openaimmlu_STEM": [
      "openaimmlu_college_biology",
      "openaimmlu_elementary_mathematics",
      "openaimmlu_high_school_mathematics",
      "openaimmlu_abstract_algebra",
      "openaimmlu_high_school_computer_science",
      "openaimmlu_conceptual_physics",
      "openaimmlu_college_mathematics",
      "openaimmlu_high_school_physics",
      "openaimmlu_high_school_biology",
      "openaimmlu_high_school_statistics",
      "openaimmlu_college_physics",
      "openaimmlu_econometrics",
      "openaimmlu_astronomy",
      "openaimmlu_high_school_chemistry",
      "openaimmlu_computer_security",
      "openaimmlu_college_computer_science",
      "openaimmlu_college_chemistry",
      "openaimmlu_electrical_engineering"
    ],
    "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_target": "gold",
      "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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  "config": {
    "model": "hf",
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  "git_hash": "5e10e017",
  "date": 1736968234.9414365,
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