evaluations/ar/jais-family-13b-chat/openaimmlu_0_shot.json

{
  "results": {
    "openaimmlu": {
      "acc,none": 0.47728243839908846,
      "acc_stderr,none": 0.004075228135853262,
      "alias": "openaimmlu"
    },
    "openaimmlu_STEM": {
      "acc,none": 0.40066225165562913,
      "acc_stderr,none": 0.008735985110676752,
      "alias": " - STEM"
    },
    "openaimmlu_abstract_algebra": {
      "alias": "  - abstract_algebra",
      "acc,none": 0.25,
      "acc_stderr,none": 0.04351941398892446
    },
    "openaimmlu_astronomy": {
      "alias": "  - astronomy",
      "acc,none": 0.5197368421052632,
      "acc_stderr,none": 0.040657710025626036
    },
    "openaimmlu_college_biology": {
      "alias": "  - college_biology",
      "acc,none": 0.5277777777777778,
      "acc_stderr,none": 0.04174752578923185
    },
    "openaimmlu_college_chemistry": {
      "alias": "  - college_chemistry",
      "acc,none": 0.34,
      "acc_stderr,none": 0.04760952285695236
    },
    "openaimmlu_college_computer_science": {
      "alias": "  - college_computer_science",
      "acc,none": 0.46,
      "acc_stderr,none": 0.05009082659620333
    },
    "openaimmlu_college_mathematics": {
      "alias": "  - college_mathematics",
      "acc,none": 0.29,
      "acc_stderr,none": 0.04560480215720684
    },
    "openaimmlu_college_physics": {
      "alias": "  - college_physics",
      "acc,none": 0.3333333333333333,
      "acc_stderr,none": 0.04690650298201943
    },
    "openaimmlu_computer_security": {
      "alias": "  - computer_security",
      "acc,none": 0.57,
      "acc_stderr,none": 0.04975698519562428
    },
    "openaimmlu_conceptual_physics": {
      "alias": "  - conceptual_physics",
      "acc,none": 0.3872340425531915,
      "acc_stderr,none": 0.03184389265339526
    },
    "openaimmlu_econometrics": {
      "alias": "  - econometrics",
      "acc,none": 0.3333333333333333,
      "acc_stderr,none": 0.044346007015849245
    },
    "openaimmlu_electrical_engineering": {
      "alias": "  - electrical_engineering",
      "acc,none": 0.4689655172413793,
      "acc_stderr,none": 0.04158632762097828
    },
    "openaimmlu_elementary_mathematics": {
      "alias": "  - elementary_mathematics",
      "acc,none": 0.3412698412698413,
      "acc_stderr,none": 0.02441923496681907
    },
    "openaimmlu_high_school_biology": {
      "alias": "  - high_school_biology",
      "acc,none": 0.5838709677419355,
      "acc_stderr,none": 0.028040981380761543
    },
    "openaimmlu_high_school_chemistry": {
      "alias": "  - high_school_chemistry",
      "acc,none": 0.4236453201970443,
      "acc_stderr,none": 0.034767257476490364
    },
    "openaimmlu_high_school_computer_science": {
      "alias": "  - high_school_computer_science",
      "acc,none": 0.49,
      "acc_stderr,none": 0.05024183937956912
    },
    "openaimmlu_high_school_mathematics": {
      "alias": "  - high_school_mathematics",
      "acc,none": 0.29259259259259257,
      "acc_stderr,none": 0.02773896963217609
    },
    "openaimmlu_high_school_physics": {
      "alias": "  - high_school_physics",
      "acc,none": 0.33112582781456956,
      "acc_stderr,none": 0.038425817186598696
    },
    "openaimmlu_high_school_statistics": {
      "alias": "  - high_school_statistics",
      "acc,none": 0.27314814814814814,
      "acc_stderr,none": 0.030388051301678116
    },
    "openaimmlu_humanities": {
      "acc,none": 0.6003325942350333,
      "acc_stderr,none": 0.011449323544037743,
      "alias": " - Humanities"
    },
    "openaimmlu_high_school_european_history": {
      "alias": "  - high_school_european_history",
      "acc,none": 0.6909090909090909,
      "acc_stderr,none": 0.036085410115739666
    },
    "openaimmlu_high_school_us_history": {
      "alias": "  - high_school_us_history",
      "acc,none": 0.6323529411764706,
      "acc_stderr,none": 0.03384132045674118
    },
    "openaimmlu_high_school_world_history": {
      "alias": "  - high_school_world_history",
      "acc,none": 0.6835443037974683,
      "acc_stderr,none": 0.03027497488021898
    },
    "openaimmlu_international_law": {
      "alias": "  - international_law",
      "acc,none": 0.6446280991735537,
      "acc_stderr,none": 0.0436923632657398
    },
    "openaimmlu_jurisprudence": {
      "alias": "  - jurisprudence",
      "acc,none": 0.5555555555555556,
      "acc_stderr,none": 0.04803752235190192
    },
    "openaimmlu_logical_fallacies": {
      "alias": "  - logical_fallacies",
      "acc,none": 0.5521472392638037,
      "acc_stderr,none": 0.03906947479456606
    },
    "openaimmlu_philosophy": {
      "alias": "  - philosophy",
      "acc,none": 0.5530546623794212,
      "acc_stderr,none": 0.028237769422085335
    },
    "openaimmlu_prehistory": {
      "alias": "  - prehistory",
      "acc,none": 0.5061728395061729,
      "acc_stderr,none": 0.027818623962583302
    },
    "openaimmlu_world_religions": {
      "alias": "  - world_religions",
      "acc,none": 0.6666666666666666,
      "acc_stderr,none": 0.036155076303109344
    },
    "openaimmlu_other": {
      "acc,none": 0.48128792987188135,
      "acc_stderr,none": 0.006333441327132957,
      "alias": " - Other"
    },
    "openaimmlu_anatomy": {
      "alias": "  - anatomy",
      "acc,none": 0.4444444444444444,
      "acc_stderr,none": 0.04292596718256981
    },
    "openaimmlu_clinical_knowledge": {
      "alias": "  - clinical_knowledge",
      "acc,none": 0.5509433962264151,
      "acc_stderr,none": 0.030612730713641095
    },
    "openaimmlu_college_medicine": {
      "alias": "  - college_medicine",
      "acc,none": 0.49710982658959535,
      "acc_stderr,none": 0.038124005659748335
    },
    "openaimmlu_formal_logic": {
      "alias": "  - formal_logic",
      "acc,none": 0.35714285714285715,
      "acc_stderr,none": 0.04285714285714281
    },
    "openaimmlu_global_facts": {
      "alias": "  - global_facts",
      "acc,none": 0.32,
      "acc_stderr,none": 0.04688261722621504
    },
    "openaimmlu_high_school_geography": {
      "alias": "  - high_school_geography",
      "acc,none": 0.6515151515151515,
      "acc_stderr,none": 0.033948539651564025
    },
    "openaimmlu_high_school_psychology": {
      "alias": "  - high_school_psychology",
      "acc,none": 0.6220183486238532,
      "acc_stderr,none": 0.020789187066728106
    },
    "openaimmlu_human_aging": {
      "alias": "  - human_aging",
      "acc,none": 0.547085201793722,
      "acc_stderr,none": 0.033408675019233246
    },
    "openaimmlu_machine_learning": {
      "alias": "  - machine_learning",
      "acc,none": 0.375,
      "acc_stderr,none": 0.04595091388086298
    },
    "openaimmlu_medical_genetics": {
      "alias": "  - medical_genetics",
      "acc,none": 0.6,
      "acc_stderr,none": 0.04923659639173309
    },
    "openaimmlu_miscellaneous": {
      "alias": "  - miscellaneous",
      "acc,none": 0.6257982120051085,
      "acc_stderr,none": 0.01730480507225203
    },
    "openaimmlu_nutrition": {
      "alias": "  - nutrition",
      "acc,none": 0.5359477124183006,
      "acc_stderr,none": 0.02855582751652878
    },
    "openaimmlu_professional_accounting": {
      "alias": "  - professional_accounting",
      "acc,none": 0.37943262411347517,
      "acc_stderr,none": 0.028947338851614105
    },
    "openaimmlu_professional_law": {
      "alias": "  - professional_law",
      "acc,none": 0.3500651890482399,
      "acc_stderr,none": 0.012182552313215175
    },
    "openaimmlu_professional_medicine": {
      "alias": "  - professional_medicine",
      "acc,none": 0.4338235294117647,
      "acc_stderr,none": 0.030105636570016633
    },
    "openaimmlu_professional_psychology": {
      "alias": "  - professional_psychology",
      "acc,none": 0.4869281045751634,
      "acc_stderr,none": 0.020220920829626912
    },
    "openaimmlu_virology": {
      "alias": "  - virology",
      "acc,none": 0.4819277108433735,
      "acc_stderr,none": 0.03889951252827216
    },
    "openaimmlu_social_science": {
      "acc,none": 0.472915398660986,
      "acc_stderr,none": 0.008280814440523745,
      "alias": " - Social Science"
    },
    "openaimmlu_business_ethics": {
      "alias": "  - business_ethics",
      "acc,none": 0.58,
      "acc_stderr,none": 0.049604496374885836
    },
    "openaimmlu_high_school_government_and_politics": {
      "alias": "  - high_school_government_and_politics",
      "acc,none": 0.694300518134715,
      "acc_stderr,none": 0.033248379397581594
    },
    "openaimmlu_high_school_macroeconomics": {
      "alias": "  - high_school_macroeconomics",
      "acc,none": 0.4846153846153846,
      "acc_stderr,none": 0.025339003010106515
    },
    "openaimmlu_high_school_microeconomics": {
      "alias": "  - high_school_microeconomics",
      "acc,none": 0.42436974789915966,
      "acc_stderr,none": 0.032104790510157764
    },
    "openaimmlu_human_sexuality": {
      "alias": "  - human_sexuality",
      "acc,none": 0.6106870229007634,
      "acc_stderr,none": 0.04276486542814591
    },
    "openaimmlu_management": {
      "alias": "  - management",
      "acc,none": 0.5825242718446602,
      "acc_stderr,none": 0.048828405482122375
    },
    "openaimmlu_marketing": {
      "alias": "  - marketing",
      "acc,none": 0.6196581196581197,
      "acc_stderr,none": 0.03180425204384099
    },
    "openaimmlu_moral_disputes": {
      "alias": "  - moral_disputes",
      "acc,none": 0.5433526011560693,
      "acc_stderr,none": 0.026817718130348916
    },
    "openaimmlu_moral_scenarios": {
      "alias": "  - moral_scenarios",
      "acc,none": 0.24022346368715083,
      "acc_stderr,none": 0.014288343803925315
    },
    "openaimmlu_public_relations": {
      "alias": "  - public_relations",
      "acc,none": 0.44545454545454544,
      "acc_stderr,none": 0.047605488214603246
    },
    "openaimmlu_security_studies": {
      "alias": "  - security_studies",
      "acc,none": 0.5836734693877551,
      "acc_stderr,none": 0.03155782816556165
    },
    "openaimmlu_sociology": {
      "alias": "  - sociology",
      "acc,none": 0.6218905472636815,
      "acc_stderr,none": 0.034288678487786564
    },
    "openaimmlu_us_foreign_policy": {
      "alias": "  - us_foreign_policy",
      "acc,none": 0.67,
      "acc_stderr,none": 0.047258156262526094
    }
  },
  "groups": {
    "openaimmlu": {
      "acc,none": 0.47728243839908846,
      "acc_stderr,none": 0.004075228135853262,
      "alias": "openaimmlu"
    },
    "openaimmlu_STEM": {
      "acc,none": 0.40066225165562913,
      "acc_stderr,none": 0.008735985110676752,
      "alias": " - STEM"
    },
    "openaimmlu_humanities": {
      "acc,none": 0.6003325942350333,
      "acc_stderr,none": 0.011449323544037743,
      "alias": " - Humanities"
    },
    "openaimmlu_other": {
      "acc,none": 0.48128792987188135,
      "acc_stderr,none": 0.006333441327132957,
      "alias": " - Other"
    },
    "openaimmlu_social_science": {
      "acc,none": 0.472915398660986,
      "acc_stderr,none": 0.008280814440523745,
      "alias": " - Social Science"
    }
  },
  "group_subtasks": {
    "openaimmlu_humanities": [
      "openaimmlu_logical_fallacies",
      "openaimmlu_philosophy",
      "openaimmlu_international_law",
      "openaimmlu_high_school_us_history",
      "openaimmlu_jurisprudence",
      "openaimmlu_prehistory",
      "openaimmlu_world_religions",
      "openaimmlu_high_school_world_history",
      "openaimmlu_high_school_european_history"
    ],
    "openaimmlu_social_science": [
      "openaimmlu_us_foreign_policy",
      "openaimmlu_high_school_microeconomics",
      "openaimmlu_moral_scenarios",
      "openaimmlu_high_school_government_and_politics",
      "openaimmlu_management",
      "openaimmlu_moral_disputes",
      "openaimmlu_public_relations",
      "openaimmlu_human_sexuality",
      "openaimmlu_security_studies",
      "openaimmlu_business_ethics",
      "openaimmlu_sociology",
      "openaimmlu_high_school_macroeconomics",
      "openaimmlu_marketing"
    ],
    "openaimmlu_other": [
      "openaimmlu_professional_law",
      "openaimmlu_professional_psychology",
      "openaimmlu_machine_learning",
      "openaimmlu_human_aging",
      "openaimmlu_high_school_geography",
      "openaimmlu_anatomy",
      "openaimmlu_college_medicine",
      "openaimmlu_professional_medicine",
      "openaimmlu_global_facts",
      "openaimmlu_medical_genetics",
      "openaimmlu_miscellaneous",
      "openaimmlu_nutrition",
      "openaimmlu_formal_logic",
      "openaimmlu_high_school_psychology",
      "openaimmlu_clinical_knowledge",
      "openaimmlu_virology",
      "openaimmlu_professional_accounting"
    ],
    "openaimmlu_STEM": [
      "openaimmlu_college_mathematics",
      "openaimmlu_abstract_algebra",
      "openaimmlu_college_biology",
      "openaimmlu_computer_security",
      "openaimmlu_high_school_biology",
      "openaimmlu_college_physics",
      "openaimmlu_high_school_physics",
      "openaimmlu_elementary_mathematics",
      "openaimmlu_conceptual_physics",
      "openaimmlu_high_school_computer_science",
      "openaimmlu_high_school_statistics",
      "openaimmlu_electrical_engineering",
      "openaimmlu_college_computer_science",
      "openaimmlu_high_school_chemistry",
      "openaimmlu_college_chemistry",
      "openaimmlu_econometrics",
      "openaimmlu_astronomy",
      "openaimmlu_high_school_mathematics"
    ],
    "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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        "version": 0.0
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