2653 lines
131 KiB
JSON
2653 lines
131 KiB
JSON
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{
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"results": {
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"openaimmlu": {
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" ": " ",
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"alias": "openaimmlu"
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"alias": " - Humanities"
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"alias": " - high_school_european_history",
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"openaimmlu_jurisprudence": {
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"alias": " - jurisprudence",
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"alias": " - Other"
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"openaimmlu_anatomy": {
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||
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"alias": " - anatomy",
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||
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||
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||
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||
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||
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||
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||
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||
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"alias": " - medical_genetics",
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||
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||
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||
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|
||
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||
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||
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||
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|
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||
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||
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|
||
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||
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|
||
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|
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|
||
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|
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||
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|
},
|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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||
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|
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|
||
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|
||
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||
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||
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||
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||
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||
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"alias": " - Social Science"
|
||
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|
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|
||
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|
||
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|
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||
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||
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||
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|
||
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||
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||
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||
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|
||
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|
||
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|
||
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|
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|
||
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||
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|
||
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|
||
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|
||
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|
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||
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||
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|
||
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|
||
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|
||
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|
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||
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|
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||
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|
||
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|
||
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|
||
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|
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|
||
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|
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||
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|
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|
||
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|
||
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|
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|
||
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|
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|
||
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|
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||
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|
||
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|
||
|
|
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|
||
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|
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|
||
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|
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||
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|
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|
||
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|
||
|
|
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|
||
|
|
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|
||
|
|
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||
|
|
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|
||
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|
||
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|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
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|
||
|
|
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|
||
|
|
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|
||
|
|
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||
|
|
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|
||
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|
||
|
|
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|
||
|
|
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|
||
|
|
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||
|
|
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|
||
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|
||
|
|
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|
||
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|
||
|
|
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|
||
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|
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|
||
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|
||
|
|
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|
||
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|
||
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|
||
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|
||
|
|
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|
||
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|
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|
||
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|
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|
||
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||
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|
||
|
|
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|
||
|
|
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|
||
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|
||
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||
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|
||
|
|
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|
||
|
|
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|
||
|
|
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|
||
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||
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|
||
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|
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|
||
|
|
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|
||
|
|
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|
||
|
|
"group_subtasks": {
|
||
|
|
"openaimmlu_humanities": [
|
||
|
|
"openaimmlu_high_school_us_history",
|
||
|
|
"openaimmlu_logical_fallacies",
|
||
|
|
"openaimmlu_jurisprudence",
|
||
|
|
"openaimmlu_world_religions",
|
||
|
|
"openaimmlu_philosophy",
|
||
|
|
"openaimmlu_high_school_world_history",
|
||
|
|
"openaimmlu_international_law",
|
||
|
|
"openaimmlu_high_school_european_history",
|
||
|
|
"openaimmlu_prehistory"
|
||
|
|
],
|
||
|
|
"openaimmlu_social_science": [
|
||
|
|
"openaimmlu_business_ethics",
|
||
|
|
"openaimmlu_high_school_government_and_politics",
|
||
|
|
"openaimmlu_marketing",
|
||
|
|
"openaimmlu_us_foreign_policy",
|
||
|
|
"openaimmlu_moral_disputes",
|
||
|
|
"openaimmlu_sociology",
|
||
|
|
"openaimmlu_high_school_macroeconomics",
|
||
|
|
"openaimmlu_management",
|
||
|
|
"openaimmlu_moral_scenarios",
|
||
|
|
"openaimmlu_human_sexuality",
|
||
|
|
"openaimmlu_security_studies",
|
||
|
|
"openaimmlu_public_relations",
|
||
|
|
"openaimmlu_high_school_microeconomics"
|
||
|
|
],
|
||
|
|
"openaimmlu_other": [
|
||
|
|
"openaimmlu_professional_medicine",
|
||
|
|
"openaimmlu_professional_psychology",
|
||
|
|
"openaimmlu_virology",
|
||
|
|
"openaimmlu_anatomy",
|
||
|
|
"openaimmlu_formal_logic",
|
||
|
|
"openaimmlu_professional_law",
|
||
|
|
"openaimmlu_human_aging",
|
||
|
|
"openaimmlu_high_school_psychology",
|
||
|
|
"openaimmlu_medical_genetics",
|
||
|
|
"openaimmlu_college_medicine",
|
||
|
|
"openaimmlu_high_school_geography",
|
||
|
|
"openaimmlu_nutrition",
|
||
|
|
"openaimmlu_machine_learning",
|
||
|
|
"openaimmlu_global_facts",
|
||
|
|
"openaimmlu_miscellaneous",
|
||
|
|
"openaimmlu_professional_accounting",
|
||
|
|
"openaimmlu_clinical_knowledge"
|
||
|
|
],
|
||
|
|
"openaimmlu_STEM": [
|
||
|
|
"openaimmlu_computer_security",
|
||
|
|
"openaimmlu_college_chemistry",
|
||
|
|
"openaimmlu_conceptual_physics",
|
||
|
|
"openaimmlu_astronomy",
|
||
|
|
"openaimmlu_high_school_mathematics",
|
||
|
|
"openaimmlu_high_school_chemistry",
|
||
|
|
"openaimmlu_high_school_physics",
|
||
|
|
"openaimmlu_high_school_biology",
|
||
|
|
"openaimmlu_abstract_algebra",
|
||
|
|
"openaimmlu_college_biology",
|
||
|
|
"openaimmlu_college_computer_science",
|
||
|
|
"openaimmlu_college_mathematics",
|
||
|
|
"openaimmlu_college_physics",
|
||
|
|
"openaimmlu_econometrics",
|
||
|
|
"openaimmlu_elementary_mathematics",
|
||
|
|
"openaimmlu_high_school_statistics",
|
||
|
|
"openaimmlu_electrical_engineering",
|
||
|
|
"openaimmlu_high_school_computer_science"
|
||
|
|
],
|
||
|
|
"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",
|
||
|
|
"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
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"versions": {
|
||
|
|
"openaimmlu_STEM": 0,
|
||
|
|
"openaimmlu_abstract_algebra": 0.0,
|
||
|
|
"openaimmlu_anatomy": 0.0,
|
||
|
|
"openaimmlu_astronomy": 0.0,
|
||
|
|
"openaimmlu_business_ethics": 0.0,
|
||
|
|
"openaimmlu_clinical_knowledge": 0.0,
|
||
|
|
"openaimmlu_college_biology": 0.0,
|
||
|
|
"openaimmlu_college_chemistry": 0.0,
|
||
|
|
"openaimmlu_college_computer_science": 0.0,
|
||
|
|
"openaimmlu_college_mathematics": 0.0,
|
||
|
|
"openaimmlu_college_medicine": 0.0,
|
||
|
|
"openaimmlu_college_physics": 0.0,
|
||
|
|
"openaimmlu_computer_security": 0.0,
|
||
|
|
"openaimmlu_conceptual_physics": 0.0,
|
||
|
|
"openaimmlu_econometrics": 0.0,
|
||
|
|
"openaimmlu_electrical_engineering": 0.0,
|
||
|
|
"openaimmlu_elementary_mathematics": 0.0,
|
||
|
|
"openaimmlu_formal_logic": 0.0,
|
||
|
|
"openaimmlu_global_facts": 0.0,
|
||
|
|
"openaimmlu_high_school_biology": 0.0,
|
||
|
|
"openaimmlu_high_school_chemistry": 0.0,
|
||
|
|
"openaimmlu_high_school_computer_science": 0.0,
|
||
|
|
"openaimmlu_high_school_european_history": 0.0,
|
||
|
|
"openaimmlu_high_school_geography": 0.0,
|
||
|
|
"openaimmlu_high_school_government_and_politics": 0.0,
|
||
|
|
"openaimmlu_high_school_macroeconomics": 0.0,
|
||
|
|
"openaimmlu_high_school_mathematics": 0.0,
|
||
|
|
"openaimmlu_high_school_microeconomics": 0.0,
|
||
|
|
"openaimmlu_high_school_physics": 0.0,
|
||
|
|
"openaimmlu_high_school_psychology": 0.0,
|
||
|
|
"openaimmlu_high_school_statistics": 0.0,
|
||
|
|
"openaimmlu_high_school_us_history": 0.0,
|
||
|
|
"openaimmlu_high_school_world_history": 0.0,
|
||
|
|
"openaimmlu_human_aging": 0.0,
|
||
|
|
"openaimmlu_human_sexuality": 0.0,
|
||
|
|
"openaimmlu_humanities": 0,
|
||
|
|
"openaimmlu_international_law": 0.0,
|
||
|
|
"openaimmlu_jurisprudence": 0.0,
|
||
|
|
"openaimmlu_logical_fallacies": 0.0,
|
||
|
|
"openaimmlu_machine_learning": 0.0,
|
||
|
|
"openaimmlu_management": 0.0,
|
||
|
|
"openaimmlu_marketing": 0.0,
|
||
|
|
"openaimmlu_medical_genetics": 0.0,
|
||
|
|
"openaimmlu_miscellaneous": 0.0,
|
||
|
|
"openaimmlu_moral_disputes": 0.0,
|
||
|
|
"openaimmlu_moral_scenarios": 0.0,
|
||
|
|
"openaimmlu_nutrition": 0.0,
|
||
|
|
"openaimmlu_other": 0,
|
||
|
|
"openaimmlu_philosophy": 0.0,
|
||
|
|
"openaimmlu_prehistory": 0.0,
|
||
|
|
"openaimmlu_professional_accounting": 0.0,
|
||
|
|
"openaimmlu_professional_law": 0.0,
|
||
|
|
"openaimmlu_professional_medicine": 0.0,
|
||
|
|
"openaimmlu_professional_psychology": 0.0,
|
||
|
|
"openaimmlu_public_relations": 0.0,
|
||
|
|
"openaimmlu_security_studies": 0.0,
|
||
|
|
"openaimmlu_social_science": 0,
|
||
|
|
"openaimmlu_sociology": 0.0,
|
||
|
|
"openaimmlu_us_foreign_policy": 0.0,
|
||
|
|
"openaimmlu_virology": 0.0,
|
||
|
|
"openaimmlu_world_religions": 0.0
|
||
|
|
},
|
||
|
|
"n-shot": {
|
||
|
|
"openaimmlu_abstract_algebra": 0,
|
||
|
|
"openaimmlu_anatomy": 0,
|
||
|
|
"openaimmlu_astronomy": 0,
|
||
|
|
"openaimmlu_business_ethics": 0,
|
||
|
|
"openaimmlu_clinical_knowledge": 0,
|
||
|
|
"openaimmlu_college_biology": 0,
|
||
|
|
"openaimmlu_college_chemistry": 0,
|
||
|
|
"openaimmlu_college_computer_science": 0,
|
||
|
|
"openaimmlu_college_mathematics": 0,
|
||
|
|
"openaimmlu_college_medicine": 0,
|
||
|
|
"openaimmlu_college_physics": 0,
|
||
|
|
"openaimmlu_computer_security": 0,
|
||
|
|
"openaimmlu_conceptual_physics": 0,
|
||
|
|
"openaimmlu_econometrics": 0,
|
||
|
|
"openaimmlu_electrical_engineering": 0,
|
||
|
|
"openaimmlu_elementary_mathematics": 0,
|
||
|
|
"openaimmlu_formal_logic": 0,
|
||
|
|
"openaimmlu_global_facts": 0,
|
||
|
|
"openaimmlu_high_school_biology": 0,
|
||
|
|
"openaimmlu_high_school_chemistry": 0,
|
||
|
|
"openaimmlu_high_school_computer_science": 0,
|
||
|
|
"openaimmlu_high_school_european_history": 0,
|
||
|
|
"openaimmlu_high_school_geography": 0,
|
||
|
|
"openaimmlu_high_school_government_and_politics": 0,
|
||
|
|
"openaimmlu_high_school_macroeconomics": 0,
|
||
|
|
"openaimmlu_high_school_mathematics": 0,
|
||
|
|
"openaimmlu_high_school_microeconomics": 0,
|
||
|
|
"openaimmlu_high_school_physics": 0,
|
||
|
|
"openaimmlu_high_school_psychology": 0,
|
||
|
|
"openaimmlu_high_school_statistics": 0,
|
||
|
|
"openaimmlu_high_school_us_history": 0,
|
||
|
|
"openaimmlu_high_school_world_history": 0,
|
||
|
|
"openaimmlu_human_aging": 0,
|
||
|
|
"openaimmlu_human_sexuality": 0,
|
||
|
|
"openaimmlu_international_law": 0,
|
||
|
|
"openaimmlu_jurisprudence": 0,
|
||
|
|
"openaimmlu_logical_fallacies": 0,
|
||
|
|
"openaimmlu_machine_learning": 0,
|
||
|
|
"openaimmlu_management": 0,
|
||
|
|
"openaimmlu_marketing": 0,
|
||
|
|
"openaimmlu_medical_genetics": 0,
|
||
|
|
"openaimmlu_miscellaneous": 0,
|
||
|
|
"openaimmlu_moral_disputes": 0,
|
||
|
|
"openaimmlu_moral_scenarios": 0,
|
||
|
|
"openaimmlu_nutrition": 0,
|
||
|
|
"openaimmlu_philosophy": 0,
|
||
|
|
"openaimmlu_prehistory": 0,
|
||
|
|
"openaimmlu_professional_accounting": 0,
|
||
|
|
"openaimmlu_professional_law": 0,
|
||
|
|
"openaimmlu_professional_medicine": 0,
|
||
|
|
"openaimmlu_professional_psychology": 0,
|
||
|
|
"openaimmlu_public_relations": 0,
|
||
|
|
"openaimmlu_security_studies": 0,
|
||
|
|
"openaimmlu_sociology": 0,
|
||
|
|
"openaimmlu_us_foreign_policy": 0,
|
||
|
|
"openaimmlu_virology": 0,
|
||
|
|
"openaimmlu_world_religions": 0
|
||
|
|
},
|
||
|
|
"higher_is_better": {
|
||
|
|
"openaimmlu": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_STEM": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_abstract_algebra": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_anatomy": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_astronomy": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_business_ethics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"openaimmlu_clinical_knowledge": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
|
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||
|
|
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|
||
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|
||
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||
|
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
|
|
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||
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||
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||
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||
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|
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||
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||
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||
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||
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||
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||
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||
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||
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||
|
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||
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||
|
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||
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||
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||
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||
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||
|
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||
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||
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||
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|
||
|
|
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||
|
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||
|
|
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||
|
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||
|
|
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||
|
|
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||
|
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||
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||
|
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||
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||
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||
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||
|
|
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||
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||
|
|
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||
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||
|
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||
|
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||
|
|
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||
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||
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||
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||
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|
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||
|
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|
||
|
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||
|
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||
|
|
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||
|
|
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||
|
|
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||
|
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||
|
|
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||
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|
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||
|
|
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||
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||
|
|
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||
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|
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||
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||
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||
|
|
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||
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||
|
|
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||
|
|
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||
|
|
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||
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||
|
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||
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||
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|
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||
|
|
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||
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||
|
|
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||
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||
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||
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||
|
|
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||
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|
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||
|
|
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||
|
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||
|
|
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||
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||
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||
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||
|
|
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||
|
|
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||
|
|
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||
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||
|
|
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||
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||
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||
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||
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||
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||
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||
|
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||
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|
||
|
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||
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|
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||
|
|
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||
|
|
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||
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||
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||
|
|
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||
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||
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|
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||
|
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||
|
|
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||
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||
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||
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||
|
|
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|
||
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|
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|
||
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||
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|
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||
|
|
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|
||
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|
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|
||
|
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||
|
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||
|
|
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|
||
|
|
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||
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||
|
|
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||
|
|
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||
|
|
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||
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|
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||
|
|
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||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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||
|
|
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|
||
|
|
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|
||
|
|
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||
|
|
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||
|
|
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|
||
|
|
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|
||
|
|
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||
|
|
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||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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||
|
|
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|
||
|
|
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||
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|
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||
|
|
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||
|
|
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|
||
|
|
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||
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|
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|
||
|
|
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||
|
|
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||
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||
|
|
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||
|
|
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||
|
|
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||
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|
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||
|
|
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||
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|
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||
|
|
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|
||
|
|
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|
||
|
|
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||
|
|
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||
|
|
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||
|
|
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||
|
|
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||
|
|
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||
|
|
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|
||
|
|
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||
|
|
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|
||
|
|
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||
|
|
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|
||
|
|
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||
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||
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|
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||
|
|
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|
||
|
|
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||
|
|
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||
|
|
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||
|
|
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|
||
|
|
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||
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|
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||
|
|
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||
|
|
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|
||
|
|
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||
|
|
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||
|
|
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||
|
|
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|
||
|
|
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||
|
|
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||
|
|
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||
|
|
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|
||
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|
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|
||
|
|
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||
|
|
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||
|
|
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|
||
|
|
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|
||
|
|
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|
||
|
|
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||
|
|
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|
||
|
|
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||
|
|
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||
|
|
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||
|
|
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||
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||
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||
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||
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