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{
"results": {
"mmlu_pro": {
"exact_match,custom-extract": 0.6276595744680851,
"exact_match_stderr,custom-extract": 0.004277657696294284,
"alias": "mmlu_pro"
},
"mmlu_pro_biology": {
"alias": " - biology",
"exact_match,custom-extract": 0.8172942817294282,
"exact_match_stderr,custom-extract": 0.01444138309804995
},
"mmlu_pro_business": {
"alias": " - business",
"exact_match,custom-extract": 0.6818757921419518,
"exact_match_stderr,custom-extract": 0.016591585393780417
},
"mmlu_pro_chemistry": {
"alias": " - chemistry",
"exact_match,custom-extract": 0.43374558303886923,
"exact_match_stderr,custom-extract": 0.014736421382027111
},
"mmlu_pro_computer_science": {
"alias": " - computer_science",
"exact_match,custom-extract": 0.697560975609756,
"exact_match_stderr,custom-extract": 0.022711632302604486
},
"mmlu_pro_economics": {
"alias": " - economics",
"exact_match,custom-extract": 0.7677725118483413,
"exact_match_stderr,custom-extract": 0.014543177498123004
},
"mmlu_pro_engineering": {
"alias": " - engineering",
"exact_match,custom-extract": 0.4674922600619195,
"exact_match_stderr,custom-extract": 0.01603660736145302
},
"mmlu_pro_health": {
"alias": " - health",
"exact_match,custom-extract": 0.6662591687041565,
"exact_match_stderr,custom-extract": 0.01649739005439522
},
"mmlu_pro_history": {
"alias": " - history",
"exact_match,custom-extract": 0.6640419947506562,
"exact_match_stderr,custom-extract": 0.02422972423970542
},
"mmlu_pro_law": {
"alias": " - law",
"exact_match,custom-extract": 0.4913714804722979,
"exact_match_stderr,custom-extract": 0.015073322269094068
},
"mmlu_pro_math": {
"alias": " - math",
"exact_match,custom-extract": 0.6321243523316062,
"exact_match_stderr,custom-extract": 0.013124564346094566
},
"mmlu_pro_other": {
"alias": " - other",
"exact_match,custom-extract": 0.7261904761904762,
"exact_match_stderr,custom-extract": 0.014677385427624142
},
"mmlu_pro_philosophy": {
"alias": " - philosophy",
"exact_match,custom-extract": 0.6152304609218436,
"exact_match_stderr,custom-extract": 0.021802414150792773
},
"mmlu_pro_physics": {
"alias": " - physics",
"exact_match,custom-extract": 0.5835257890685143,
"exact_match_stderr,custom-extract": 0.013683170484760148
},
"mmlu_pro_psychology": {
"alias": " - psychology",
"exact_match,custom-extract": 0.7781954887218046,
"exact_match_stderr,custom-extract": 0.014716359253560095
}
},
"groups": {
"mmlu_pro": {
"exact_match,custom-extract": 0.6276595744680851,
"exact_match_stderr,custom-extract": 0.004277657696294284,
"alias": "mmlu_pro"
}
},
"group_subtasks": {
"mmlu_pro": [
"mmlu_pro_biology",
"mmlu_pro_business",
"mmlu_pro_chemistry",
"mmlu_pro_computer_science",
"mmlu_pro_economics",
"mmlu_pro_engineering",
"mmlu_pro_health",
"mmlu_pro_history",
"mmlu_pro_law",
"mmlu_pro_math",
"mmlu_pro_other",
"mmlu_pro_philosophy",
"mmlu_pro_physics",
"mmlu_pro_psychology"
]
},
"configs": {
"mmlu_pro_biology": {
"task": "mmlu_pro_biology",
"task_alias": "biology",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab4885d80>, subject='biology')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab4886a70>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about biology. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab4887760>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_business": {
"task": "mmlu_pro_business",
"task_alias": "business",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab4887640>, subject='business')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab4884700>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about business. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab48851b0>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_chemistry": {
"task": "mmlu_pro_chemistry",
"task_alias": "chemistry",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab4886b90>, subject='chemistry')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab4886d40>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about chemistry. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab4884f70>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_computer_science": {
"task": "mmlu_pro_computer_science",
"task_alias": "computer_science",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab4885990>, subject='computer science')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab4886830>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about computer science. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab4885fc0>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_economics": {
"task": "mmlu_pro_economics",
"task_alias": "economics",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab4884160>, subject='economics')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab48840d0>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about economics. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab48843a0>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_engineering": {
"task": "mmlu_pro_engineering",
"task_alias": "engineering",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab65496c0>, subject='engineering')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab65497e0>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about engineering. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab65492d0>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_health": {
"task": "mmlu_pro_health",
"task_alias": "health",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab654b010>, subject='health')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab654bbe0>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about health. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab6549d80>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_history": {
"task": "mmlu_pro_history",
"task_alias": "history",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab6549fc0>, subject='history')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab6549090>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about history. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab6549510>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_law": {
"task": "mmlu_pro_law",
"task_alias": "law",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab654ba30>, subject='law')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab6549ea0>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about law. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab654add0>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_math": {
"task": "mmlu_pro_math",
"task_alias": "math",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab6549b40>, subject='math')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab654b2e0>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about math. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab654b400>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_other": {
"task": "mmlu_pro_other",
"task_alias": "other",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab6548430>, subject='other')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab654a8c0>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about other. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab6548700>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_philosophy": {
"task": "mmlu_pro_philosophy",
"task_alias": "philosophy",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab6c805e0>, subject='philosophy')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab6548310>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about philosophy. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab654acb0>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_physics": {
"task": "mmlu_pro_physics",
"task_alias": "physics",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab6549240>, subject='physics')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab6548af0>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about physics. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab6548dc0>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"mmlu_pro_psychology": {
"task": "mmlu_pro_psychology",
"task_alias": "psychology",
"dataset_path": "TIGER-Lab/MMLU-Pro",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "functools.partial(<function process_docs at 0x147ab6c804c0>, subject='psychology')",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab6c36170>, including_answer=False)",
"doc_to_target": "answer",
"description": "The following are multiple choice questions (with answers) about psychology. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"doc_to_text": "functools.partial(<function format_cot_example at 0x147ab6c370a0>, including_answer=True)",
"doc_to_target": ""
},
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"</s>",
"Q:",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "custom-extract",
"filter": [
{
"function": "regex",
"regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
}
},
"versions": {
"mmlu_pro": 2.0,
"mmlu_pro_biology": 1.0,
"mmlu_pro_business": 1.0,
"mmlu_pro_chemistry": 1.0,
"mmlu_pro_computer_science": 1.0,
"mmlu_pro_economics": 1.0,
"mmlu_pro_engineering": 1.0,
"mmlu_pro_health": 1.0,
"mmlu_pro_history": 1.0,
"mmlu_pro_law": 1.0,
"mmlu_pro_math": 1.0,
"mmlu_pro_other": 1.0,
"mmlu_pro_philosophy": 1.0,
"mmlu_pro_physics": 1.0,
"mmlu_pro_psychology": 1.0
},
"n-shot": {
"mmlu_pro_biology": 5,
"mmlu_pro_business": 5,
"mmlu_pro_chemistry": 5,
"mmlu_pro_computer_science": 5,
"mmlu_pro_economics": 5,
"mmlu_pro_engineering": 5,
"mmlu_pro_health": 5,
"mmlu_pro_history": 5,
"mmlu_pro_law": 5,
"mmlu_pro_math": 5,
"mmlu_pro_other": 5,
"mmlu_pro_philosophy": 5,
"mmlu_pro_physics": 5,
"mmlu_pro_psychology": 5
},
"higher_is_better": {
"mmlu_pro": {
"exact_match": true
},
"mmlu_pro_biology": {
"exact_match": true
},
"mmlu_pro_business": {
"exact_match": true
},
"mmlu_pro_chemistry": {
"exact_match": true
},
"mmlu_pro_computer_science": {
"exact_match": true
},
"mmlu_pro_economics": {
"exact_match": true
},
"mmlu_pro_engineering": {
"exact_match": true
},
"mmlu_pro_health": {
"exact_match": true
},
"mmlu_pro_history": {
"exact_match": true
},
"mmlu_pro_law": {
"exact_match": true
},
"mmlu_pro_math": {
"exact_match": true
},
"mmlu_pro_other": {
"exact_match": true
},
"mmlu_pro_philosophy": {
"exact_match": true
},
"mmlu_pro_physics": {
"exact_match": true
},
"mmlu_pro_psychology": {
"exact_match": true
}
},
"n-samples": {
"mmlu_pro_biology": {
"original": 717,
"effective": 717
},
"mmlu_pro_business": {
"original": 789,
"effective": 789
},
"mmlu_pro_chemistry": {
"original": 1132,
"effective": 1132
},
"mmlu_pro_computer_science": {
"original": 410,
"effective": 410
},
"mmlu_pro_economics": {
"original": 844,
"effective": 844
},
"mmlu_pro_engineering": {
"original": 969,
"effective": 969
},
"mmlu_pro_health": {
"original": 818,
"effective": 818
},
"mmlu_pro_history": {
"original": 381,
"effective": 381
},
"mmlu_pro_law": {
"original": 1101,
"effective": 1101
},
"mmlu_pro_math": {
"original": 1351,
"effective": 1351
},
"mmlu_pro_other": {
"original": 924,
"effective": 924
},
"mmlu_pro_philosophy": {
"original": 499,
"effective": 499
},
"mmlu_pro_physics": {
"original": 1299,
"effective": 1299
},
"mmlu_pro_psychology": {
"original": 798,
"effective": 798
}
},
"config": {
"model": "vllm",
"model_args": "pretrained=Qwen/Qwen2.5-72B-Instruct,tensor_parallel_size=4,data_parallel_size=2,gpu_memory_utilization=0.98,download_dir=/tmp,enforce_eager=True",
"batch_size": 1,
"batch_sizes": [],
"device": null,
"use_cache": null,
"limit": null,
"bootstrap_iters": 100000,
"gen_kwargs": null,
"random_seed": 0,
"numpy_seed": 1234,
"torch_seed": 1234,
"fewshot_seed": 1234
},
"git_hash": "8e1bd48d",
"date": 1735993548.5607338,
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"transformers_version": "4.47.1",
"upper_git_hash": "f64fe2f2a86055aaecced603b56097fd79201711",
"tokenizer_pad_token": [
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],
"tokenizer_eos_token": [
"<|im_end|>",
"151645"
],
"tokenizer_bos_token": [
null,
"None"
],
"eot_token_id": 151645,
"max_length": 32768,
"task_hashes": {},
"model_source": "vllm",
"model_name": "Qwen/Qwen2.5-72B-Instruct",
"model_name_sanitized": "Qwen__Qwen2.5-72B-Instruct",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 60213.334928124,
"end_time": 62387.667025258,
"total_evaluation_time_seconds": "2174.332097134"
} |