2051 lines
100 KiB
JSON
2051 lines
100 KiB
JSON
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{
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"results": {
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||
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||
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"arabicmmlu_middle_natural_science",
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"arabicmmlu_high_physics",
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||
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||
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"arabicmmlu_primary_general_knowledge",
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|
|
"task": "arabicmmlu_arabic_language_(general)",
|
||
|
|
"task_alias": "Arabic Language (General)",
|
||
|
|
"tag": "arabicmmlu_language_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Arabic Language (General)",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_arabic_language_(grammar)": {
|
||
|
|
"task": "arabicmmlu_arabic_language_(grammar)",
|
||
|
|
"task_alias": "Arabic Language (Grammar)",
|
||
|
|
"tag": "arabicmmlu_language_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Arabic Language (Grammar)",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_driving_test": {
|
||
|
|
"task": "arabicmmlu_driving_test",
|
||
|
|
"task_alias": "Driving Test",
|
||
|
|
"tag": "arabicmmlu_other_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Driving Test",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_general_knowledge": {
|
||
|
|
"task": "arabicmmlu_general_knowledge",
|
||
|
|
"task_alias": "General Knowledge",
|
||
|
|
"tag": "arabicmmlu_other_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "General Knowledge",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_arabic_language": {
|
||
|
|
"task": "arabicmmlu_high_arabic_language",
|
||
|
|
"task_alias": "High Arabic Language",
|
||
|
|
"tag": "arabicmmlu_language_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "High Arabic Language",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_biology": {
|
||
|
|
"task": "arabicmmlu_high_biology",
|
||
|
|
"task_alias": "High Biology",
|
||
|
|
"tag": "arabicmmlu_stem_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "High Biology",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_civics": {
|
||
|
|
"task": "arabicmmlu_high_civics",
|
||
|
|
"task_alias": "High Civics",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "High Civics",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_computer_science": {
|
||
|
|
"task": "arabicmmlu_high_computer_science",
|
||
|
|
"task_alias": "High Computer Science",
|
||
|
|
"tag": "arabicmmlu_stem_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "High Computer Science",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_economics": {
|
||
|
|
"task": "arabicmmlu_high_economics",
|
||
|
|
"task_alias": "High Economics",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "High Economics",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_geography": {
|
||
|
|
"task": "arabicmmlu_high_geography",
|
||
|
|
"task_alias": "High Geography",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "High Geography",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_history": {
|
||
|
|
"task": "arabicmmlu_high_history",
|
||
|
|
"task_alias": "High History",
|
||
|
|
"tag": "arabicmmlu_humanities_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "High History",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_islamic_studies": {
|
||
|
|
"task": "arabicmmlu_high_islamic_studies",
|
||
|
|
"task_alias": "High Islamic Studies",
|
||
|
|
"tag": "arabicmmlu_humanities_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "High Islamic Studies",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_philosophy": {
|
||
|
|
"task": "arabicmmlu_high_philosophy",
|
||
|
|
"task_alias": "High Philosophy",
|
||
|
|
"tag": "arabicmmlu_humanities_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "High Philosophy",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_physics": {
|
||
|
|
"task": "arabicmmlu_high_physics",
|
||
|
|
"task_alias": "High Physics",
|
||
|
|
"tag": "arabicmmlu_stem_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "High Physics",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_islamic_studies": {
|
||
|
|
"task": "arabicmmlu_islamic_studies",
|
||
|
|
"task_alias": "Islamic Studies",
|
||
|
|
"tag": "arabicmmlu_humanities_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Islamic Studies",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_arabic_language": {
|
||
|
|
"task": "arabicmmlu_middle_arabic_language",
|
||
|
|
"task_alias": "Middle Arabic Language",
|
||
|
|
"tag": "arabicmmlu_language_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Middle Arabic Language",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_civics": {
|
||
|
|
"task": "arabicmmlu_middle_civics",
|
||
|
|
"task_alias": "Middle Civics",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Middle Civics",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_computer_science": {
|
||
|
|
"task": "arabicmmlu_middle_computer_science",
|
||
|
|
"task_alias": "Middle Computer Science",
|
||
|
|
"tag": "arabicmmlu_stem_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Middle Computer Science",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_economics": {
|
||
|
|
"task": "arabicmmlu_middle_economics",
|
||
|
|
"task_alias": "Middle Economics",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Middle Economics",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_general_knowledge": {
|
||
|
|
"task": "arabicmmlu_middle_general_knowledge",
|
||
|
|
"task_alias": "Middle General Knowledge",
|
||
|
|
"tag": "arabicmmlu_other_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Middle General Knowledge",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_geography": {
|
||
|
|
"task": "arabicmmlu_middle_geography",
|
||
|
|
"task_alias": "Middle Geography",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Middle Geography",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_history": {
|
||
|
|
"task": "arabicmmlu_middle_history",
|
||
|
|
"task_alias": "Middle History",
|
||
|
|
"tag": "arabicmmlu_humanities_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Middle History",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_islamic_studies": {
|
||
|
|
"task": "arabicmmlu_middle_islamic_studies",
|
||
|
|
"task_alias": "Middle Islamic Studies",
|
||
|
|
"tag": "arabicmmlu_humanities_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Middle Islamic Studies",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_natural_science": {
|
||
|
|
"task": "arabicmmlu_middle_natural_science",
|
||
|
|
"task_alias": "Middle Natural Science",
|
||
|
|
"tag": "arabicmmlu_stem_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Middle Natural Science",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_social_science": {
|
||
|
|
"task": "arabicmmlu_middle_social_science",
|
||
|
|
"task_alias": "Middle Social Science",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Middle Social Science",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_arabic_language": {
|
||
|
|
"task": "arabicmmlu_primary_arabic_language",
|
||
|
|
"task_alias": "Primary Arabic Language",
|
||
|
|
"tag": "arabicmmlu_language_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Primary Arabic Language",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_computer_science": {
|
||
|
|
"task": "arabicmmlu_primary_computer_science",
|
||
|
|
"task_alias": "Primary Computer Science",
|
||
|
|
"tag": "arabicmmlu_stem_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Primary Computer Science",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_general_knowledge": {
|
||
|
|
"task": "arabicmmlu_primary_general_knowledge",
|
||
|
|
"task_alias": "Primary General Knowledge",
|
||
|
|
"tag": "arabicmmlu_other_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Primary General Knowledge",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_geography": {
|
||
|
|
"task": "arabicmmlu_primary_geography",
|
||
|
|
"task_alias": "Primary Geography",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Primary Geography",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_history": {
|
||
|
|
"task": "arabicmmlu_primary_history",
|
||
|
|
"task_alias": "Primary History",
|
||
|
|
"tag": "arabicmmlu_humanities_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Primary History",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_islamic_studies": {
|
||
|
|
"task": "arabicmmlu_primary_islamic_studies",
|
||
|
|
"task_alias": "Primary Islamic Studies",
|
||
|
|
"tag": "arabicmmlu_humanities_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Primary Islamic Studies",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_math": {
|
||
|
|
"task": "arabicmmlu_primary_math",
|
||
|
|
"task_alias": "Primary Math",
|
||
|
|
"tag": "arabicmmlu_stem_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Primary Math",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_natural_science": {
|
||
|
|
"task": "arabicmmlu_primary_natural_science",
|
||
|
|
"task_alias": "Primary Natural Science",
|
||
|
|
"tag": "arabicmmlu_stem_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Primary Natural Science",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_social_science": {
|
||
|
|
"task": "arabicmmlu_primary_social_science",
|
||
|
|
"task_alias": "Primary Social Science",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Primary Social Science",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_prof_law": {
|
||
|
|
"task": "arabicmmlu_prof_law",
|
||
|
|
"task_alias": "Prof Law",
|
||
|
|
"tag": "arabicmmlu_humanities_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Prof Law",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_accounting": {
|
||
|
|
"task": "arabicmmlu_univ_accounting",
|
||
|
|
"task_alias": "Univ Accounting",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Univ Accounting",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_computer_science": {
|
||
|
|
"task": "arabicmmlu_univ_computer_science",
|
||
|
|
"task_alias": "Univ Computer Science",
|
||
|
|
"tag": "arabicmmlu_stem_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Univ Computer Science",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_economics": {
|
||
|
|
"task": "arabicmmlu_univ_economics",
|
||
|
|
"task_alias": "Univ Economics",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Univ Economics",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_management": {
|
||
|
|
"task": "arabicmmlu_univ_management",
|
||
|
|
"task_alias": "Univ Management",
|
||
|
|
"tag": "arabicmmlu_other_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Univ Management",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_political_science": {
|
||
|
|
"task": "arabicmmlu_univ_political_science",
|
||
|
|
"task_alias": "Univ Political Science",
|
||
|
|
"tag": "arabicmmlu_social_science_tasks",
|
||
|
|
"dataset_path": "yazeed7/ArabicMMLU",
|
||
|
|
"dataset_name": "Univ Political Science",
|
||
|
|
"test_split": "test",
|
||
|
|
"fewshot_split": "dev",
|
||
|
|
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Refactoring `prepare_data_en` to fit with the lm harness framework.\n https://github.com/mbzuai-nlp/ArabicMMLU/blob/main/util_prompt.py\n \"\"\"\n\n level = \"\" if not doc[\"Level\"] else \" for \" + level_en[doc[\"Level\"]]\n country = \"\" if not doc[\"Country\"] else \" in \" + doc[\"Country\"]\n main_meta_data = f\"{doc['Subject']} question{level}{country}\"\n\n question = (\n doc[\"Question\"]\n if doc[\"Context\"] == \"\"\n else f\"{doc['Context']}\\n\\n{doc['Question']}\"\n )\n\n options = []\n for i, opt in enumerate(\n [\"Option 1\", \"Option 2\", \"Option 3\", \"Option 4\", \"Option 5\"]\n ):\n if not doc[opt]:\n break\n options.append(f\"{alpa[i]} {doc[opt]}\")\n\n doc_text = PROMPT.format(main_meta_data, question, \"\\n\".join(options))\n\n return doc_text\n",
|
||
|
|
"doc_to_target": "Answer Key",
|
||
|
|
"doc_to_choice": "def doc_to_choice(doc):\n return [alpa[i][0] for i in range(5) if doc[f\"Option {i+1}\"]]\n",
|
||
|
|
"description": "",
|
||
|
|
"target_delimiter": " ",
|
||
|
|
"fewshot_delimiter": "\n\n",
|
||
|
|
"fewshot_config": {
|
||
|
|
"sampler": "first_n"
|
||
|
|
},
|
||
|
|
"num_fewshot": 0,
|
||
|
|
"metric_list": [
|
||
|
|
{
|
||
|
|
"metric": "acc",
|
||
|
|
"aggregation": "mean",
|
||
|
|
"higher_is_better": true
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"output_type": "multiple_choice",
|
||
|
|
"repeats": 1,
|
||
|
|
"should_decontaminate": false,
|
||
|
|
"metadata": {
|
||
|
|
"version": 0.0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"versions": {
|
||
|
|
"arabicmmlu": 0,
|
||
|
|
"arabicmmlu_arabic_language_(general)": 0.0,
|
||
|
|
"arabicmmlu_arabic_language_(grammar)": 0.0,
|
||
|
|
"arabicmmlu_driving_test": 0.0,
|
||
|
|
"arabicmmlu_general_knowledge": 0.0,
|
||
|
|
"arabicmmlu_high_arabic_language": 0.0,
|
||
|
|
"arabicmmlu_high_biology": 0.0,
|
||
|
|
"arabicmmlu_high_civics": 0.0,
|
||
|
|
"arabicmmlu_high_computer_science": 0.0,
|
||
|
|
"arabicmmlu_high_economics": 0.0,
|
||
|
|
"arabicmmlu_high_geography": 0.0,
|
||
|
|
"arabicmmlu_high_history": 0.0,
|
||
|
|
"arabicmmlu_high_islamic_studies": 0.0,
|
||
|
|
"arabicmmlu_high_philosophy": 0.0,
|
||
|
|
"arabicmmlu_high_physics": 0.0,
|
||
|
|
"arabicmmlu_humanities": 0,
|
||
|
|
"arabicmmlu_islamic_studies": 0.0,
|
||
|
|
"arabicmmlu_language": 0,
|
||
|
|
"arabicmmlu_middle_arabic_language": 0.0,
|
||
|
|
"arabicmmlu_middle_civics": 0.0,
|
||
|
|
"arabicmmlu_middle_computer_science": 0.0,
|
||
|
|
"arabicmmlu_middle_economics": 0.0,
|
||
|
|
"arabicmmlu_middle_general_knowledge": 0.0,
|
||
|
|
"arabicmmlu_middle_geography": 0.0,
|
||
|
|
"arabicmmlu_middle_history": 0.0,
|
||
|
|
"arabicmmlu_middle_islamic_studies": 0.0,
|
||
|
|
"arabicmmlu_middle_natural_science": 0.0,
|
||
|
|
"arabicmmlu_middle_social_science": 0.0,
|
||
|
|
"arabicmmlu_other": 0,
|
||
|
|
"arabicmmlu_primary_arabic_language": 0.0,
|
||
|
|
"arabicmmlu_primary_computer_science": 0.0,
|
||
|
|
"arabicmmlu_primary_general_knowledge": 0.0,
|
||
|
|
"arabicmmlu_primary_geography": 0.0,
|
||
|
|
"arabicmmlu_primary_history": 0.0,
|
||
|
|
"arabicmmlu_primary_islamic_studies": 0.0,
|
||
|
|
"arabicmmlu_primary_math": 0.0,
|
||
|
|
"arabicmmlu_primary_natural_science": 0.0,
|
||
|
|
"arabicmmlu_primary_social_science": 0.0,
|
||
|
|
"arabicmmlu_prof_law": 0.0,
|
||
|
|
"arabicmmlu_social_science": 0,
|
||
|
|
"arabicmmlu_stem": 0,
|
||
|
|
"arabicmmlu_univ_accounting": 0.0,
|
||
|
|
"arabicmmlu_univ_computer_science": 0.0,
|
||
|
|
"arabicmmlu_univ_economics": 0.0,
|
||
|
|
"arabicmmlu_univ_management": 0.0,
|
||
|
|
"arabicmmlu_univ_political_science": 0.0
|
||
|
|
},
|
||
|
|
"n-shot": {
|
||
|
|
"arabicmmlu_arabic_language_(general)": 0,
|
||
|
|
"arabicmmlu_arabic_language_(grammar)": 0,
|
||
|
|
"arabicmmlu_driving_test": 0,
|
||
|
|
"arabicmmlu_general_knowledge": 0,
|
||
|
|
"arabicmmlu_high_arabic_language": 0,
|
||
|
|
"arabicmmlu_high_biology": 0,
|
||
|
|
"arabicmmlu_high_civics": 0,
|
||
|
|
"arabicmmlu_high_computer_science": 0,
|
||
|
|
"arabicmmlu_high_economics": 0,
|
||
|
|
"arabicmmlu_high_geography": 0,
|
||
|
|
"arabicmmlu_high_history": 0,
|
||
|
|
"arabicmmlu_high_islamic_studies": 0,
|
||
|
|
"arabicmmlu_high_philosophy": 0,
|
||
|
|
"arabicmmlu_high_physics": 0,
|
||
|
|
"arabicmmlu_islamic_studies": 0,
|
||
|
|
"arabicmmlu_middle_arabic_language": 0,
|
||
|
|
"arabicmmlu_middle_civics": 0,
|
||
|
|
"arabicmmlu_middle_computer_science": 0,
|
||
|
|
"arabicmmlu_middle_economics": 0,
|
||
|
|
"arabicmmlu_middle_general_knowledge": 0,
|
||
|
|
"arabicmmlu_middle_geography": 0,
|
||
|
|
"arabicmmlu_middle_history": 0,
|
||
|
|
"arabicmmlu_middle_islamic_studies": 0,
|
||
|
|
"arabicmmlu_middle_natural_science": 0,
|
||
|
|
"arabicmmlu_middle_social_science": 0,
|
||
|
|
"arabicmmlu_primary_arabic_language": 0,
|
||
|
|
"arabicmmlu_primary_computer_science": 0,
|
||
|
|
"arabicmmlu_primary_general_knowledge": 0,
|
||
|
|
"arabicmmlu_primary_geography": 0,
|
||
|
|
"arabicmmlu_primary_history": 0,
|
||
|
|
"arabicmmlu_primary_islamic_studies": 0,
|
||
|
|
"arabicmmlu_primary_math": 0,
|
||
|
|
"arabicmmlu_primary_natural_science": 0,
|
||
|
|
"arabicmmlu_primary_social_science": 0,
|
||
|
|
"arabicmmlu_prof_law": 0,
|
||
|
|
"arabicmmlu_univ_accounting": 0,
|
||
|
|
"arabicmmlu_univ_computer_science": 0,
|
||
|
|
"arabicmmlu_univ_economics": 0,
|
||
|
|
"arabicmmlu_univ_management": 0,
|
||
|
|
"arabicmmlu_univ_political_science": 0
|
||
|
|
},
|
||
|
|
"higher_is_better": {
|
||
|
|
"arabicmmlu": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_arabic_language_(general)": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_arabic_language_(grammar)": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_driving_test": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_general_knowledge": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_arabic_language": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_biology": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_civics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_computer_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_economics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_geography": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_history": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_islamic_studies": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_philosophy": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_physics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_humanities": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_islamic_studies": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_language": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_arabic_language": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_civics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_computer_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_economics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_general_knowledge": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_geography": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_history": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_islamic_studies": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_natural_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_social_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_other": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_arabic_language": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_computer_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_general_knowledge": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_geography": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_history": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_islamic_studies": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_math": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_natural_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_social_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_prof_law": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_social_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_stem": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_accounting": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_computer_science": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_economics": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_management": {
|
||
|
|
"acc": true
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_political_science": {
|
||
|
|
"acc": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"n-samples": {
|
||
|
|
"arabicmmlu_general_knowledge": {
|
||
|
|
"original": 864,
|
||
|
|
"effective": 864
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_general_knowledge": {
|
||
|
|
"original": 172,
|
||
|
|
"effective": 172
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_general_knowledge": {
|
||
|
|
"original": 162,
|
||
|
|
"effective": 162
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_management": {
|
||
|
|
"original": 75,
|
||
|
|
"effective": 75
|
||
|
|
},
|
||
|
|
"arabicmmlu_driving_test": {
|
||
|
|
"original": 1211,
|
||
|
|
"effective": 1211
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_civics": {
|
||
|
|
"original": 87,
|
||
|
|
"effective": 87
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_political_science": {
|
||
|
|
"original": 210,
|
||
|
|
"effective": 210
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_economics": {
|
||
|
|
"original": 360,
|
||
|
|
"effective": 360
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_economics": {
|
||
|
|
"original": 87,
|
||
|
|
"effective": 87
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_economics": {
|
||
|
|
"original": 137,
|
||
|
|
"effective": 137
|
||
|
|
},
|
||
|
|
"arabicmmlu_high_geography": {
|
||
|
|
"original": 1038,
|
||
|
|
"effective": 1038
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_geography": {
|
||
|
|
"original": 57,
|
||
|
|
"effective": 57
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_civics": {
|
||
|
|
"original": 236,
|
||
|
|
"effective": 236
|
||
|
|
},
|
||
|
|
"arabicmmlu_univ_accounting": {
|
||
|
|
"original": 74,
|
||
|
|
"effective": 74
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_social_science": {
|
||
|
|
"original": 241,
|
||
|
|
"effective": 241
|
||
|
|
},
|
||
|
|
"arabicmmlu_middle_geography": {
|
||
|
|
"original": 272,
|
||
|
|
"effective": 272
|
||
|
|
},
|
||
|
|
"arabicmmlu_primary_social_science": {
|
||
|
|
"original": 705,
|
||
|
|
"effective": 705
|
||
|
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|
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||
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|
||
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|
||
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||
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|
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"config": {
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16
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"device": null,
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"date": 1737858946.4669714,
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"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.89\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (24 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Mitigation; untrained return thunk; SMT disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort:
|
||
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|
"transformers_version": "4.48.1",
|
||
|
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"upper_git_hash": "086919bd66f4e15fdcd4b792a7b27a698c1ba091",
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"tokenizer_pad_token": [
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"128004"
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],
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|
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||
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"system_instruction": null,
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"chat_template": null,
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"start_time": 820233.226282937,
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"end_time": 821135.688521802,
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"total_evaluation_time_seconds": "902.4622388649732"
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}
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