Rename results_2024-05-20T17-28-23.843554.json to evals/results_2024-05-20T17-28-23.843554.json
11a123b
verified
| { | |
| "results": { | |
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| "acc_norm,none": 0.532130777903044, | |
| "acc_norm_stderr,none": 0.00827855765089745, | |
| "alias": "ai2_arc" | |
| } | |
| }, | |
| "group_subtasks": { | |
| "ai2_arc": [ | |
| "arc_easy", | |
| "arc_challenge" | |
| ], | |
| "gsm8k": [], | |
| "hellaswag": [], | |
| "mmlu_stem": [ | |
| "mmlu_machine_learning", | |
| "mmlu_high_school_statistics", | |
| "mmlu_high_school_physics", | |
| "mmlu_high_school_mathematics", | |
| "mmlu_high_school_computer_science", | |
| "mmlu_high_school_chemistry", | |
| "mmlu_high_school_biology", | |
| "mmlu_elementary_mathematics", | |
| "mmlu_electrical_engineering", | |
| "mmlu_conceptual_physics", | |
| "mmlu_computer_security", | |
| "mmlu_college_physics", | |
| "mmlu_college_mathematics", | |
| "mmlu_college_computer_science", | |
| "mmlu_college_chemistry", | |
| "mmlu_college_biology", | |
| "mmlu_astronomy", | |
| "mmlu_anatomy", | |
| "mmlu_abstract_algebra" | |
| ], | |
| "mmlu_other": [ | |
| "mmlu_virology", | |
| "mmlu_professional_medicine", | |
| "mmlu_professional_accounting", | |
| "mmlu_nutrition", | |
| "mmlu_miscellaneous", | |
| "mmlu_medical_genetics", | |
| "mmlu_marketing", | |
| "mmlu_management", | |
| "mmlu_human_aging", | |
| "mmlu_global_facts", | |
| "mmlu_college_medicine", | |
| "mmlu_clinical_knowledge", | |
| "mmlu_business_ethics" | |
| ], | |
| "mmlu_social_sciences": [ | |
| "mmlu_us_foreign_policy", | |
| "mmlu_sociology", | |
| "mmlu_security_studies", | |
| "mmlu_public_relations", | |
| "mmlu_professional_psychology", | |
| "mmlu_human_sexuality", | |
| "mmlu_high_school_psychology", | |
| "mmlu_high_school_microeconomics", | |
| "mmlu_high_school_macroeconomics", | |
| "mmlu_high_school_government_and_politics", | |
| "mmlu_high_school_geography", | |
| "mmlu_econometrics" | |
| ], | |
| "mmlu_humanities": [ | |
| "mmlu_world_religions", | |
| "mmlu_professional_law", | |
| "mmlu_prehistory", | |
| "mmlu_philosophy", | |
| "mmlu_moral_scenarios", | |
| "mmlu_moral_disputes", | |
| "mmlu_logical_fallacies", | |
| "mmlu_jurisprudence", | |
| "mmlu_international_law", | |
| "mmlu_high_school_world_history", | |
| "mmlu_high_school_us_history", | |
| "mmlu_high_school_european_history", | |
| "mmlu_formal_logic" | |
| ], | |
| "mmlu": [ | |
| "mmlu_humanities", | |
| "mmlu_social_sciences", | |
| "mmlu_other", | |
| "mmlu_stem" | |
| ], | |
| "truthfulqa": [ | |
| "truthfulqa_mc2", | |
| "truthfulqa_mc1", | |
| "truthfulqa_gen" | |
| ], | |
| "winogrande": [] | |
| }, | |
| "configs": { | |
| "arc_challenge": { | |
| "task": "arc_challenge", | |
| "group": [ | |
| "ai2_arc" | |
| ], | |
| "dataset_path": "allenai/ai2_arc", | |
| "dataset_name": "ARC-Challenge", | |
| "training_split": "train", | |
| "validation_split": "validation", | |
| "test_split": "test", | |
| "doc_to_text": "Question: {{question}}\nAnswer:", | |
| "doc_to_target": "{{choices.label.index(answerKey)}}", | |
| "doc_to_choice": "{{choices.text}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": true, | |
| "doc_to_decontamination_query": "Question: {{question}}\nAnswer:", | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "arc_easy": { | |
| "task": "arc_easy", | |
| "group": [ | |
| "ai2_arc" | |
| ], | |
| "dataset_path": "allenai/ai2_arc", | |
| "dataset_name": "ARC-Easy", | |
| "training_split": "train", | |
| "validation_split": "validation", | |
| "test_split": "test", | |
| "doc_to_text": "Question: {{question}}\nAnswer:", | |
| "doc_to_target": "{{choices.label.index(answerKey)}}", | |
| "doc_to_choice": "{{choices.text}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": true, | |
| "doc_to_decontamination_query": "Question: {{question}}\nAnswer:", | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "gsm8k": { | |
| "task": "gsm8k", | |
| "group": [ | |
| "math_word_problems" | |
| ], | |
| "dataset_path": "gsm8k", | |
| "dataset_name": "main", | |
| "training_split": "train", | |
| "test_split": "test", | |
| "fewshot_split": "train", | |
| "doc_to_text": "Question: {{question}}\nAnswer:", | |
| "doc_to_target": "{{answer}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 5, | |
| "metric_list": [ | |
| { | |
| "metric": "exact_match", | |
| "aggregation": "mean", | |
| "higher_is_better": true, | |
| "ignore_case": true, | |
| "ignore_punctuation": false, | |
| "regexes_to_ignore": [ | |
| ",", | |
| "\\$", | |
| "(?s).*#### ", | |
| "\\.$" | |
| ] | |
| } | |
| ], | |
| "output_type": "generate_until", | |
| "generation_kwargs": { | |
| "until": [ | |
| "Question:", | |
| "</s>", | |
| "<|im_end|>" | |
| ], | |
| "do_sample": false, | |
| "temperature": 0.0 | |
| }, | |
| "repeats": 1, | |
| "filter_list": [ | |
| { | |
| "name": "strict-match", | |
| "filter": [ | |
| { | |
| "function": "regex", | |
| "regex_pattern": "#### (\\-?[0-9\\.\\,]+)" | |
| }, | |
| { | |
| "function": "take_first" | |
| } | |
| ] | |
| }, | |
| { | |
| "name": "flexible-extract", | |
| "filter": [ | |
| { | |
| "function": "regex", | |
| "group_select": -1, | |
| "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)" | |
| }, | |
| { | |
| "function": "take_first" | |
| } | |
| ] | |
| } | |
| ], | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 3.0 | |
| } | |
| }, | |
| "hellaswag": { | |
| "task": "hellaswag", | |
| "group": [ | |
| "multiple_choice" | |
| ], | |
| "dataset_path": "hellaswag", | |
| "training_split": "train", | |
| "validation_split": "validation", | |
| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", | |
| "doc_to_text": "{{query}}", | |
| "doc_to_target": "{{label}}", | |
| "doc_to_choice": "choices", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "acc_norm", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| }, | |
| "mmlu_abstract_algebra": { | |
| "task": "mmlu_abstract_algebra", | |
| "task_alias": "abstract_algebra", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "abstract_algebra", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about abstract algebra.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_anatomy": { | |
| "task": "mmlu_anatomy", | |
| "task_alias": "anatomy", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "anatomy", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about anatomy.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_astronomy": { | |
| "task": "mmlu_astronomy", | |
| "task_alias": "astronomy", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "astronomy", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about astronomy.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_business_ethics": { | |
| "task": "mmlu_business_ethics", | |
| "task_alias": "business_ethics", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "business_ethics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about business ethics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_clinical_knowledge": { | |
| "task": "mmlu_clinical_knowledge", | |
| "task_alias": "clinical_knowledge", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "clinical_knowledge", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about clinical knowledge.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_college_biology": { | |
| "task": "mmlu_college_biology", | |
| "task_alias": "college_biology", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_biology", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about college biology.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_college_chemistry": { | |
| "task": "mmlu_college_chemistry", | |
| "task_alias": "college_chemistry", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_chemistry", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about college chemistry.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_college_computer_science": { | |
| "task": "mmlu_college_computer_science", | |
| "task_alias": "college_computer_science", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_computer_science", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about college computer science.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_college_mathematics": { | |
| "task": "mmlu_college_mathematics", | |
| "task_alias": "college_mathematics", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_mathematics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about college mathematics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_college_medicine": { | |
| "task": "mmlu_college_medicine", | |
| "task_alias": "college_medicine", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_medicine", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about college medicine.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_college_physics": { | |
| "task": "mmlu_college_physics", | |
| "task_alias": "college_physics", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_physics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about college physics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_computer_security": { | |
| "task": "mmlu_computer_security", | |
| "task_alias": "computer_security", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "computer_security", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about computer security.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_conceptual_physics": { | |
| "task": "mmlu_conceptual_physics", | |
| "task_alias": "conceptual_physics", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "conceptual_physics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about conceptual physics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_econometrics": { | |
| "task": "mmlu_econometrics", | |
| "task_alias": "econometrics", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "econometrics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about econometrics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_electrical_engineering": { | |
| "task": "mmlu_electrical_engineering", | |
| "task_alias": "electrical_engineering", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "electrical_engineering", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about electrical engineering.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_elementary_mathematics": { | |
| "task": "mmlu_elementary_mathematics", | |
| "task_alias": "elementary_mathematics", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "elementary_mathematics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about elementary mathematics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_formal_logic": { | |
| "task": "mmlu_formal_logic", | |
| "task_alias": "formal_logic", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "formal_logic", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about formal logic.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_global_facts": { | |
| "task": "mmlu_global_facts", | |
| "task_alias": "global_facts", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "global_facts", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about global facts.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_biology": { | |
| "task": "mmlu_high_school_biology", | |
| "task_alias": "high_school_biology", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_biology", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school biology.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_chemistry": { | |
| "task": "mmlu_high_school_chemistry", | |
| "task_alias": "high_school_chemistry", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_chemistry", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school chemistry.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_computer_science": { | |
| "task": "mmlu_high_school_computer_science", | |
| "task_alias": "high_school_computer_science", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_computer_science", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school computer science.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_european_history": { | |
| "task": "mmlu_high_school_european_history", | |
| "task_alias": "high_school_european_history", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_european_history", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school european history.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_geography": { | |
| "task": "mmlu_high_school_geography", | |
| "task_alias": "high_school_geography", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_geography", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school geography.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_government_and_politics": { | |
| "task": "mmlu_high_school_government_and_politics", | |
| "task_alias": "high_school_government_and_politics", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_government_and_politics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school government and politics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_macroeconomics": { | |
| "task": "mmlu_high_school_macroeconomics", | |
| "task_alias": "high_school_macroeconomics", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_macroeconomics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school macroeconomics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_mathematics": { | |
| "task": "mmlu_high_school_mathematics", | |
| "task_alias": "high_school_mathematics", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_mathematics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school mathematics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_microeconomics": { | |
| "task": "mmlu_high_school_microeconomics", | |
| "task_alias": "high_school_microeconomics", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_microeconomics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school microeconomics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_physics": { | |
| "task": "mmlu_high_school_physics", | |
| "task_alias": "high_school_physics", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_physics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school physics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_psychology": { | |
| "task": "mmlu_high_school_psychology", | |
| "task_alias": "high_school_psychology", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_psychology", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school psychology.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_statistics": { | |
| "task": "mmlu_high_school_statistics", | |
| "task_alias": "high_school_statistics", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_statistics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school statistics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_us_history": { | |
| "task": "mmlu_high_school_us_history", | |
| "task_alias": "high_school_us_history", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_us_history", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school us history.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_high_school_world_history": { | |
| "task": "mmlu_high_school_world_history", | |
| "task_alias": "high_school_world_history", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_world_history", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about high school world history.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_human_aging": { | |
| "task": "mmlu_human_aging", | |
| "task_alias": "human_aging", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "human_aging", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about human aging.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_human_sexuality": { | |
| "task": "mmlu_human_sexuality", | |
| "task_alias": "human_sexuality", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "human_sexuality", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about human sexuality.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_international_law": { | |
| "task": "mmlu_international_law", | |
| "task_alias": "international_law", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "international_law", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about international law.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_jurisprudence": { | |
| "task": "mmlu_jurisprudence", | |
| "task_alias": "jurisprudence", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "jurisprudence", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about jurisprudence.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_logical_fallacies": { | |
| "task": "mmlu_logical_fallacies", | |
| "task_alias": "logical_fallacies", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "logical_fallacies", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about logical fallacies.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_machine_learning": { | |
| "task": "mmlu_machine_learning", | |
| "task_alias": "machine_learning", | |
| "group": "mmlu_stem", | |
| "group_alias": "stem", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "machine_learning", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about machine learning.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_management": { | |
| "task": "mmlu_management", | |
| "task_alias": "management", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "management", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about management.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_marketing": { | |
| "task": "mmlu_marketing", | |
| "task_alias": "marketing", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "marketing", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about marketing.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_medical_genetics": { | |
| "task": "mmlu_medical_genetics", | |
| "task_alias": "medical_genetics", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "medical_genetics", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about medical genetics.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_miscellaneous": { | |
| "task": "mmlu_miscellaneous", | |
| "task_alias": "miscellaneous", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "miscellaneous", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about miscellaneous.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_moral_disputes": { | |
| "task": "mmlu_moral_disputes", | |
| "task_alias": "moral_disputes", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "moral_disputes", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about moral disputes.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_moral_scenarios": { | |
| "task": "mmlu_moral_scenarios", | |
| "task_alias": "moral_scenarios", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "moral_scenarios", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about moral scenarios.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_nutrition": { | |
| "task": "mmlu_nutrition", | |
| "task_alias": "nutrition", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "nutrition", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about nutrition.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_philosophy": { | |
| "task": "mmlu_philosophy", | |
| "task_alias": "philosophy", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "philosophy", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about philosophy.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_prehistory": { | |
| "task": "mmlu_prehistory", | |
| "task_alias": "prehistory", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "prehistory", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about prehistory.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_professional_accounting": { | |
| "task": "mmlu_professional_accounting", | |
| "task_alias": "professional_accounting", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "professional_accounting", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about professional accounting.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_professional_law": { | |
| "task": "mmlu_professional_law", | |
| "task_alias": "professional_law", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "professional_law", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about professional law.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_professional_medicine": { | |
| "task": "mmlu_professional_medicine", | |
| "task_alias": "professional_medicine", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "professional_medicine", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about professional medicine.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_professional_psychology": { | |
| "task": "mmlu_professional_psychology", | |
| "task_alias": "professional_psychology", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "professional_psychology", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about professional psychology.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_public_relations": { | |
| "task": "mmlu_public_relations", | |
| "task_alias": "public_relations", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "public_relations", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about public relations.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_security_studies": { | |
| "task": "mmlu_security_studies", | |
| "task_alias": "security_studies", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "security_studies", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about security studies.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_sociology": { | |
| "task": "mmlu_sociology", | |
| "task_alias": "sociology", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "sociology", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about sociology.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_us_foreign_policy": { | |
| "task": "mmlu_us_foreign_policy", | |
| "task_alias": "us_foreign_policy", | |
| "group": "mmlu_social_sciences", | |
| "group_alias": "social_sciences", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "us_foreign_policy", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about us foreign policy.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_virology": { | |
| "task": "mmlu_virology", | |
| "task_alias": "virology", | |
| "group": "mmlu_other", | |
| "group_alias": "other", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "virology", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about virology.\n\n", | |
| "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 | |
| } | |
| }, | |
| "mmlu_world_religions": { | |
| "task": "mmlu_world_religions", | |
| "task_alias": "world_religions", | |
| "group": "mmlu_humanities", | |
| "group_alias": "humanities", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "world_religions", | |
| "test_split": "test", | |
| "fewshot_split": "dev", | |
| "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:", | |
| "doc_to_target": "answer", | |
| "doc_to_choice": [ | |
| "A", | |
| "B", | |
| "C", | |
| "D" | |
| ], | |
| "description": "The following are multiple choice questions (with answers) about world religions.\n\n", | |
| "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 | |
| } | |
| }, | |
| "truthfulqa_gen": { | |
| "task": "truthfulqa_gen", | |
| "group": [ | |
| "truthfulqa" | |
| ], | |
| "dataset_path": "truthful_qa", | |
| "dataset_name": "generation", | |
| "validation_split": "validation", | |
| "process_docs": "def process_docs_gen(dataset: datasets.Dataset) -> datasets.Dataset:\n return dataset.map(preprocess_function)\n", | |
| "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question}}", | |
| "doc_to_target": " ", | |
| "process_results": "def process_results_gen(doc, results):\n completion = results[0]\n true_refs, false_refs = doc[\"correct_answers\"], doc[\"incorrect_answers\"]\n all_refs = true_refs + false_refs\n\n # Process the sentence-level BLEURT, BLEU, and ROUGE for similarity measures.\n\n # # BLEURT\n # bleurt_scores_true = self.bleurt.compute(\n # predictions=[completion] * len(true_refs), references=true_refs\n # )[\"scores\"]\n # bleurt_scores_false = self.bleurt.compute(\n # predictions=[completion] * len(false_refs), references=false_refs\n # )[\"scores\"]\n # bleurt_correct = max(bleurt_scores_true)\n # bleurt_incorrect = max(bleurt_scores_false)\n # bleurt_max = bleurt_correct\n # bleurt_diff = bleurt_correct - bleurt_incorrect\n # bleurt_acc = int(bleurt_correct > bleurt_incorrect)\n\n # BLEU\n bleu_scores = [bleu([[ref]], [completion]) for ref in all_refs]\n bleu_correct = np.nanmax(bleu_scores[: len(true_refs)])\n bleu_incorrect = np.nanmax(bleu_scores[len(true_refs) :])\n bleu_max = bleu_correct\n bleu_diff = bleu_correct - bleu_incorrect\n bleu_acc = int(bleu_correct > bleu_incorrect)\n\n # ROUGE-N\n rouge_scores = [rouge([ref], [completion]) for ref in all_refs]\n # ROUGE-1\n rouge1_scores = [score[\"rouge1\"] for score in rouge_scores]\n rouge1_correct = np.nanmax(rouge1_scores[: len(true_refs)])\n rouge1_incorrect = np.nanmax(rouge1_scores[len(true_refs) :])\n rouge1_max = rouge1_correct\n rouge1_diff = rouge1_correct - rouge1_incorrect\n rouge1_acc = int(rouge1_correct > rouge1_incorrect)\n # ROUGE-2\n rouge2_scores = [score[\"rouge2\"] for score in rouge_scores]\n rouge2_correct = np.nanmax(rouge2_scores[: len(true_refs)])\n rouge2_incorrect = np.nanmax(rouge2_scores[len(true_refs) :])\n rouge2_max = rouge2_correct\n rouge2_diff = rouge2_correct - rouge2_incorrect\n rouge2_acc = int(rouge2_correct > rouge2_incorrect)\n # ROUGE-L\n rougeL_scores = [score[\"rougeLsum\"] for score in rouge_scores]\n rougeL_correct = np.nanmax(rougeL_scores[: len(true_refs)])\n rougeL_incorrect = np.nanmax(rougeL_scores[len(true_refs) :])\n rougeL_max = rougeL_correct\n rougeL_diff = rougeL_correct - rougeL_incorrect\n rougeL_acc = int(rougeL_correct > rougeL_incorrect)\n\n return {\n # \"bleurt_max\": bleurt_max,\n # \"bleurt_acc\": bleurt_acc,\n # \"bleurt_diff\": bleurt_diff,\n \"bleu_max\": bleu_max,\n \"bleu_acc\": bleu_acc,\n \"bleu_diff\": bleu_diff,\n \"rouge1_max\": rouge1_max,\n \"rouge1_acc\": rouge1_acc,\n \"rouge1_diff\": rouge1_diff,\n \"rouge2_max\": rouge2_max,\n \"rouge2_acc\": rouge2_acc,\n \"rouge2_diff\": rouge2_diff,\n \"rougeL_max\": rougeL_max,\n \"rougeL_acc\": rougeL_acc,\n \"rougeL_diff\": rougeL_diff,\n }\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "bleu_max", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "bleu_acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "bleu_diff", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "rouge1_max", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "rouge1_acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "rouge1_diff", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "rouge2_max", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "rouge2_acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "rouge2_diff", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "rougeL_max", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "rougeL_acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| }, | |
| { | |
| "metric": "rougeL_diff", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "generate_until", | |
| "generation_kwargs": { | |
| "until": [ | |
| "\n\n" | |
| ], | |
| "do_sample": false | |
| }, | |
| "repeats": 1, | |
| "should_decontaminate": true, | |
| "doc_to_decontamination_query": "question", | |
| "metadata": { | |
| "version": 3.0 | |
| } | |
| }, | |
| "truthfulqa_mc1": { | |
| "task": "truthfulqa_mc1", | |
| "group": [ | |
| "truthfulqa" | |
| ], | |
| "dataset_path": "truthful_qa", | |
| "dataset_name": "multiple_choice", | |
| "validation_split": "validation", | |
| "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}", | |
| "doc_to_target": 0, | |
| "doc_to_choice": "{{mc1_targets.choices}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": true, | |
| "doc_to_decontamination_query": "question", | |
| "metadata": { | |
| "version": 2.0 | |
| } | |
| }, | |
| "truthfulqa_mc2": { | |
| "task": "truthfulqa_mc2", | |
| "group": [ | |
| "truthfulqa" | |
| ], | |
| "dataset_path": "truthful_qa", | |
| "dataset_name": "multiple_choice", | |
| "validation_split": "validation", | |
| "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}", | |
| "doc_to_target": 0, | |
| "doc_to_choice": "{{mc2_targets.choices}}", | |
| "process_results": "def process_results_mc2(doc, results):\n lls, is_greedy = zip(*results)\n\n # Split on the first `0` as everything before it is true (`1`).\n split_idx = list(doc[\"mc2_targets\"][\"labels\"]).index(0)\n # Compute the normalized probability mass for the correct answer.\n ll_true, ll_false = lls[:split_idx], lls[split_idx:]\n p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))\n p_true = p_true / (sum(p_true) + sum(p_false))\n\n return {\"acc\": sum(p_true)}\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": true, | |
| "doc_to_decontamination_query": "question", | |
| "metadata": { | |
| "version": 2.0 | |
| } | |
| }, | |
| "winogrande": { | |
| "task": "winogrande", | |
| "dataset_path": "winogrande", | |
| "dataset_name": "winogrande_xl", | |
| "training_split": "train", | |
| "validation_split": "validation", | |
| "doc_to_text": "def doc_to_text(doc):\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n", | |
| "doc_to_target": "def doc_to_target(doc):\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n", | |
| "doc_to_choice": "def doc_to_choice(doc):\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": true, | |
| "doc_to_decontamination_query": "sentence", | |
| "metadata": { | |
| "version": 1.0 | |
| } | |
| } | |
| }, | |
| "versions": { | |
| "arc_challenge": 1.0, | |
| "arc_easy": 1.0, | |
| "gsm8k": 3.0, | |
| "hellaswag": 1.0, | |
| "mmlu_abstract_algebra": 0.0, | |
| "mmlu_anatomy": 0.0, | |
| "mmlu_astronomy": 0.0, | |
| "mmlu_business_ethics": 0.0, | |
| "mmlu_clinical_knowledge": 0.0, | |
| "mmlu_college_biology": 0.0, | |
| "mmlu_college_chemistry": 0.0, | |
| "mmlu_college_computer_science": 0.0, | |
| "mmlu_college_mathematics": 0.0, | |
| "mmlu_college_medicine": 0.0, | |
| "mmlu_college_physics": 0.0, | |
| "mmlu_computer_security": 0.0, | |
| "mmlu_conceptual_physics": 0.0, | |
| "mmlu_econometrics": 0.0, | |
| "mmlu_electrical_engineering": 0.0, | |
| "mmlu_elementary_mathematics": 0.0, | |
| "mmlu_formal_logic": 0.0, | |
| "mmlu_global_facts": 0.0, | |
| "mmlu_high_school_biology": 0.0, | |
| "mmlu_high_school_chemistry": 0.0, | |
| "mmlu_high_school_computer_science": 0.0, | |
| "mmlu_high_school_european_history": 0.0, | |
| "mmlu_high_school_geography": 0.0, | |
| "mmlu_high_school_government_and_politics": 0.0, | |
| "mmlu_high_school_macroeconomics": 0.0, | |
| "mmlu_high_school_mathematics": 0.0, | |
| "mmlu_high_school_microeconomics": 0.0, | |
| "mmlu_high_school_physics": 0.0, | |
| "mmlu_high_school_psychology": 0.0, | |
| "mmlu_high_school_statistics": 0.0, | |
| "mmlu_high_school_us_history": 0.0, | |
| "mmlu_high_school_world_history": 0.0, | |
| "mmlu_human_aging": 0.0, | |
| "mmlu_human_sexuality": 0.0, | |
| "mmlu_international_law": 0.0, | |
| "mmlu_jurisprudence": 0.0, | |
| "mmlu_logical_fallacies": 0.0, | |
| "mmlu_machine_learning": 0.0, | |
| "mmlu_management": 0.0, | |
| "mmlu_marketing": 0.0, | |
| "mmlu_medical_genetics": 0.0, | |
| "mmlu_miscellaneous": 0.0, | |
| "mmlu_moral_disputes": 0.0, | |
| "mmlu_moral_scenarios": 0.0, | |
| "mmlu_nutrition": 0.0, | |
| "mmlu_philosophy": 0.0, | |
| "mmlu_prehistory": 0.0, | |
| "mmlu_professional_accounting": 0.0, | |
| "mmlu_professional_law": 0.0, | |
| "mmlu_professional_medicine": 0.0, | |
| "mmlu_professional_psychology": 0.0, | |
| "mmlu_public_relations": 0.0, | |
| "mmlu_security_studies": 0.0, | |
| "mmlu_sociology": 0.0, | |
| "mmlu_us_foreign_policy": 0.0, | |
| "mmlu_virology": 0.0, | |
| "mmlu_world_religions": 0.0, | |
| "truthfulqa_gen": 3.0, | |
| "truthfulqa_mc1": 2.0, | |
| "truthfulqa_mc2": 2.0, | |
| "winogrande": 1.0 | |
| }, | |
| "n-shot": { | |
| "ai2_arc": 0, | |
| "arc_challenge": 0, | |
| "arc_easy": 0, | |
| "gsm8k": 5, | |
| "hellaswag": 0, | |
| "mmlu": 0, | |
| "mmlu_abstract_algebra": 0, | |
| "mmlu_anatomy": 0, | |
| "mmlu_astronomy": 0, | |
| "mmlu_business_ethics": 0, | |
| "mmlu_clinical_knowledge": 0, | |
| "mmlu_college_biology": 0, | |
| "mmlu_college_chemistry": 0, | |
| "mmlu_college_computer_science": 0, | |
| "mmlu_college_mathematics": 0, | |
| "mmlu_college_medicine": 0, | |
| "mmlu_college_physics": 0, | |
| "mmlu_computer_security": 0, | |
| "mmlu_conceptual_physics": 0, | |
| "mmlu_econometrics": 0, | |
| "mmlu_electrical_engineering": 0, | |
| "mmlu_elementary_mathematics": 0, | |
| "mmlu_formal_logic": 0, | |
| "mmlu_global_facts": 0, | |
| "mmlu_high_school_biology": 0, | |
| "mmlu_high_school_chemistry": 0, | |
| "mmlu_high_school_computer_science": 0, | |
| "mmlu_high_school_european_history": 0, | |
| "mmlu_high_school_geography": 0, | |
| "mmlu_high_school_government_and_politics": 0, | |
| "mmlu_high_school_macroeconomics": 0, | |
| "mmlu_high_school_mathematics": 0, | |
| "mmlu_high_school_microeconomics": 0, | |
| "mmlu_high_school_physics": 0, | |
| "mmlu_high_school_psychology": 0, | |
| "mmlu_high_school_statistics": 0, | |
| "mmlu_high_school_us_history": 0, | |
| "mmlu_high_school_world_history": 0, | |
| "mmlu_human_aging": 0, | |
| "mmlu_human_sexuality": 0, | |
| "mmlu_humanities": 0, | |
| "mmlu_international_law": 0, | |
| "mmlu_jurisprudence": 0, | |
| "mmlu_logical_fallacies": 0, | |
| "mmlu_machine_learning": 0, | |
| "mmlu_management": 0, | |
| "mmlu_marketing": 0, | |
| "mmlu_medical_genetics": 0, | |
| "mmlu_miscellaneous": 0, | |
| "mmlu_moral_disputes": 0, | |
| "mmlu_moral_scenarios": 0, | |
| "mmlu_nutrition": 0, | |
| "mmlu_other": 0, | |
| "mmlu_philosophy": 0, | |
| "mmlu_prehistory": 0, | |
| "mmlu_professional_accounting": 0, | |
| "mmlu_professional_law": 0, | |
| "mmlu_professional_medicine": 0, | |
| "mmlu_professional_psychology": 0, | |
| "mmlu_public_relations": 0, | |
| "mmlu_security_studies": 0, | |
| "mmlu_social_sciences": 0, | |
| "mmlu_sociology": 0, | |
| "mmlu_stem": 0, | |
| "mmlu_us_foreign_policy": 0, | |
| "mmlu_virology": 0, | |
| "mmlu_world_religions": 0, | |
| "truthfulqa": 0, | |
| "truthfulqa_gen": 0, | |
| "truthfulqa_mc1": 0, | |
| "truthfulqa_mc2": 0, | |
| "winogrande": 0 | |
| }, | |
| "n-samples": { | |
| "winogrande": { | |
| "original": 1267, | |
| "effective": 1267 | |
| }, | |
| "truthfulqa_mc2": { | |
| "original": 817, | |
| "effective": 817 | |
| }, | |
| "truthfulqa_mc1": { | |
| "original": 817, | |
| "effective": 817 | |
| }, | |
| "truthfulqa_gen": { | |
| "original": 817, | |
| "effective": 817 | |
| }, | |
| "mmlu_world_religions": { | |
| "original": 171, | |
| "effective": 171 | |
| }, | |
| "mmlu_professional_law": { | |
| "original": 1534, | |
| "effective": 1534 | |
| }, | |
| "mmlu_prehistory": { | |
| "original": 324, | |
| "effective": 324 | |
| }, | |
| "mmlu_philosophy": { | |
| "original": 311, | |
| "effective": 311 | |
| }, | |
| "mmlu_moral_scenarios": { | |
| "original": 895, | |
| "effective": 895 | |
| }, | |
| "mmlu_moral_disputes": { | |
| "original": 346, | |
| "effective": 346 | |
| }, | |
| "mmlu_logical_fallacies": { | |
| "original": 163, | |
| "effective": 163 | |
| }, | |
| "mmlu_jurisprudence": { | |
| "original": 108, | |
| "effective": 108 | |
| }, | |
| "mmlu_international_law": { | |
| "original": 121, | |
| "effective": 121 | |
| }, | |
| "mmlu_high_school_world_history": { | |
| "original": 237, | |
| "effective": 237 | |
| }, | |
| "mmlu_high_school_us_history": { | |
| "original": 204, | |
| "effective": 204 | |
| }, | |
| "mmlu_high_school_european_history": { | |
| "original": 165, | |
| "effective": 165 | |
| }, | |
| "mmlu_formal_logic": { | |
| "original": 126, | |
| "effective": 126 | |
| }, | |
| "mmlu_us_foreign_policy": { | |
| "original": 100, | |
| "effective": 100 | |
| }, | |
| "mmlu_sociology": { | |
| "original": 201, | |
| "effective": 201 | |
| }, | |
| "mmlu_security_studies": { | |
| "original": 245, | |
| "effective": 245 | |
| }, | |
| "mmlu_public_relations": { | |
| "original": 110, | |
| "effective": 110 | |
| }, | |
| "mmlu_professional_psychology": { | |
| "original": 612, | |
| "effective": 612 | |
| }, | |
| "mmlu_human_sexuality": { | |
| "original": 131, | |
| "effective": 131 | |
| }, | |
| "mmlu_high_school_psychology": { | |
| "original": 545, | |
| "effective": 545 | |
| }, | |
| "mmlu_high_school_microeconomics": { | |
| "original": 238, | |
| "effective": 238 | |
| }, | |
| "mmlu_high_school_macroeconomics": { | |
| "original": 390, | |
| "effective": 390 | |
| }, | |
| "mmlu_high_school_government_and_politics": { | |
| "original": 193, | |
| "effective": 193 | |
| }, | |
| "mmlu_high_school_geography": { | |
| "original": 198, | |
| "effective": 198 | |
| }, | |
| "mmlu_econometrics": { | |
| "original": 114, | |
| "effective": 114 | |
| }, | |
| "mmlu_virology": { | |
| "original": 166, | |
| "effective": 166 | |
| }, | |
| "mmlu_professional_medicine": { | |
| "original": 272, | |
| "effective": 272 | |
| }, | |
| "mmlu_professional_accounting": { | |
| "original": 282, | |
| "effective": 282 | |
| }, | |
| "mmlu_nutrition": { | |
| "original": 306, | |
| "effective": 306 | |
| }, | |
| "mmlu_miscellaneous": { | |
| "original": 783, | |
| "effective": 783 | |
| }, | |
| "mmlu_medical_genetics": { | |
| "original": 100, | |
| "effective": 100 | |
| }, | |
| "mmlu_marketing": { | |
| "original": 234, | |
| "effective": 234 | |
| }, | |
| "mmlu_management": { | |
| "original": 103, | |
| "effective": 103 | |
| }, | |
| "mmlu_human_aging": { | |
| "original": 223, | |
| "effective": 223 | |
| }, | |
| "mmlu_global_facts": { | |
| "original": 100, | |
| "effective": 100 | |
| }, | |
| "mmlu_college_medicine": { | |
| "original": 173, | |
| "effective": 173 | |
| }, | |
| "mmlu_clinical_knowledge": { | |
| "original": 265, | |
| "effective": 265 | |
| }, | |
| "mmlu_business_ethics": { | |
| "original": 100, | |
| "effective": 100 | |
| }, | |
| "mmlu_machine_learning": { | |
| "original": 112, | |
| "effective": 112 | |
| }, | |
| "mmlu_high_school_statistics": { | |
| "original": 216, | |
| "effective": 216 | |
| }, | |
| "mmlu_high_school_physics": { | |
| "original": 151, | |
| "effective": 151 | |
| }, | |
| "mmlu_high_school_mathematics": { | |
| "original": 270, | |
| "effective": 270 | |
| }, | |
| "mmlu_high_school_computer_science": { | |
| "original": 100, | |
| "effective": 100 | |
| }, | |
| "mmlu_high_school_chemistry": { | |
| "original": 203, | |
| "effective": 203 | |
| }, | |
| "mmlu_high_school_biology": { | |
| "original": 310, | |
| "effective": 310 | |
| }, | |
| "mmlu_elementary_mathematics": { | |
| "original": 378, | |
| "effective": 378 | |
| }, | |
| "mmlu_electrical_engineering": { | |
| "original": 145, | |
| "effective": 145 | |
| }, | |
| "mmlu_conceptual_physics": { | |
| "original": 235, | |
| "effective": 235 | |
| }, | |
| "mmlu_computer_security": { | |
| "original": 100, | |
| "effective": 100 | |
| }, | |
| "mmlu_college_physics": { | |
| "original": 102, | |
| "effective": 102 | |
| }, | |
| "mmlu_college_mathematics": { | |
| "original": 100, | |
| "effective": 100 | |
| }, | |
| "mmlu_college_computer_science": { | |
| "original": 100, | |
| "effective": 100 | |
| }, | |
| "mmlu_college_chemistry": { | |
| "original": 100, | |
| "effective": 100 | |
| }, | |
| "mmlu_college_biology": { | |
| "original": 144, | |
| "effective": 144 | |
| }, | |
| "mmlu_astronomy": { | |
| "original": 152, | |
| "effective": 152 | |
| }, | |
| "mmlu_anatomy": { | |
| "original": 135, | |
| "effective": 135 | |
| }, | |
| "mmlu_abstract_algebra": { | |
| "original": 100, | |
| "effective": 100 | |
| }, | |
| "hellaswag": { | |
| "original": 10042, | |
| "effective": 10042 | |
| }, | |
| "gsm8k": { | |
| "original": 1319, | |
| "effective": 1319 | |
| }, | |
| "arc_easy": { | |
| "original": 2376, | |
| "effective": 2376 | |
| }, | |
| "arc_challenge": { | |
| "original": 1172, | |
| "effective": 1172 | |
| } | |
| }, | |
| "config": { | |
| "model": "hf", | |
| "model_args": "pretrained=prince-canuma/Llama-3-6B-v0", | |
| "model_num_parameters": 6285365248, | |
| "model_dtype": "torch.float16", | |
| "model_revision": "main", | |
| "model_sha": "e64555b64f4908550437c92088e038c4b48458e4", | |
| "batch_size": "16", | |
| "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": "86319a9b", | |
| "date": 1716225077.7613318, | |
| "pretty_env_info": "PyTorch version: 2.2.0+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: Could not collect\nLibc version: glibc-2.35\n\nPython version: 3.10.14 (main, Mar 21 2024, 16:24:04) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.3.103\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA RTX 6000 Ada Generation\nGPU 1: NVIDIA RTX 6000 Ada Generation\nGPU 2: NVIDIA RTX 6000 Ada Generation\nGPU 3: NVIDIA RTX 6000 Ada Generation\n\nNvidia driver version: 535.104.05\ncuDNN version: Could not collect\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: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 224\nOn-line CPU(s) list: 0-111,113-223\nOff-line CPU(s) list: 112\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 9554 64-Core Processor\nCPU family: 25\nModel: 17\nThread(s) per core: 2\nCore(s) per socket: 56\nSocket(s): 2\nStepping: 1\nFrequency boost: enabled\nCPU max MHz: 3762.9880\nCPU min MHz: 0.0000\nBogoMIPS: 6190.21\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 amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d\nVirtualization: AMD-V\nL1d cache: 3.5 MiB (112 instances)\nL1i cache: 3.5 MiB (112 instances)\nL2 cache: 112 MiB (112 instances)\nL3 cache: 512 MiB (16 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-55,113-167\nNUMA node1 CPU(s): 56-111,168-223\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: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] mypy-extensions==1.0.0\n[pip3] numpy==1.26.3\n[pip3] torch==2.2.0+cu121\n[pip3] torchaudio==2.2.0+cu121\n[pip3] torchvision==0.17.0+cu121\n[pip3] triton==2.2.0\n[conda] numpy 1.26.3 pypi_0 pypi\n[conda] torch 2.2.0+cu121 pypi_0 pypi\n[conda] torchaudio 2.2.0+cu121 pypi_0 pypi\n[conda] torchvision 0.17.0+cu121 pypi_0 pypi\n[conda] triton 2.2.0 pypi_0 pypi", | |
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| "truthfulqa_mc1": "c7109772fbb0a36564be1812f543128b074545db71587fe4bbb3dcd38a4d9010", | |
| "truthfulqa_gen": "010d2e95da0f843300e57bf7b5ae587dd1ad8f180967d80b3727a403ed0643dc", | |
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| "mmlu_philosophy": "9164df2c285cf3cde6dbf36f535a435e61a1eb883063fc2d3b9dfc894859389b", | |
| "mmlu_moral_scenarios": "3a3d861731fb55a1fdca6af9844e207b8f404e9304bd578d4087ef8b3170ef2e", | |
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| "mmlu_logical_fallacies": "7b1df4cf6360dde813fb8989186d545b5e61ae7bc2e4e21038b70acb31400201", | |
| "mmlu_jurisprudence": "8809ca6a44e52fd3f7202788261f8bd59e1952269d583a879bde696b6932790f", | |
| "mmlu_international_law": "818fca9a03d79be8c16531adf0a71d72ba289c62a78bdd7e41038e1ee973df9e", | |
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| "mmlu_high_school_us_history": "45731c2798fa2c2f35028269f8e4ccbe75e1a681389d8fdcbd6aff0e3f8854d9", | |
| "mmlu_high_school_european_history": "91ff07284d39e44598a277d1568021be02b6add3d0554c0076955f566a216264", | |
| "mmlu_formal_logic": "751d7858ca8b3bea2f2a69ba720c614eb49e77a2c49b4794afd59195342372b3", | |
| "mmlu_us_foreign_policy": "813f8d7440a3bc75c0408c543b0ac05108e4e3bfbae9d3ccf8a4f3e17c146065", | |
| "mmlu_sociology": "4e0b684c64f959c4a2f9d994c37c2b364f728e55c8eff8c6fd0b7093ad4d3f41", | |
| "mmlu_security_studies": "fd27ebd2b2ee8a4ff838ec46878acf8908cc4b29e6dd8e86be70f729f6ee0c92", | |
| "mmlu_public_relations": "1566f06b24512ce2f9e32268c91758e4bdac9e33d34c08dec0fa5c42e06058c5", | |
| "mmlu_professional_psychology": "cd93cd1d051d378d85b60cc430d3713e877c657ac44db3634b5c91142d732cbc", | |
| "mmlu_human_sexuality": "aa652ee47fde8eb9d75e3760be356fb157acd5f88bbc98d7ea8dc315aabeb563", | |
| "mmlu_high_school_psychology": "65e2395033d229addd95b0904fd2fcd9ae947b1eaeb1e6ea9c3888bde59d509a", | |
| "mmlu_high_school_microeconomics": "b2949c51595c6d71f200ba83af8609ac5a3f6d7fe8d0d0ca7a32ce4a37878860", | |
| "mmlu_high_school_macroeconomics": "0a54b5bc027641ff0875ddabb04f90e584b42001078d305cfd6b062dc65d594b", | |
| "mmlu_high_school_government_and_politics": "c4f7e9f170f8d5e745d3aef85af576c448dcc63762c614e789e0170735b908ab", | |
| "mmlu_high_school_geography": "f58fc0b8e46b8345a7eb87af5f32721372896c2dbee3204089c030b7cb543f46", | |
| "mmlu_econometrics": "b55a168be345737e1719298ea3cf179fe83c4f92a2bc14fb530785aa84e4161f", | |
| "mmlu_virology": "d8bc3b27037622b5c8e6645db8b80e90ecf765f7b0e86a44ad8c94a5c5ec119f", | |
| "mmlu_professional_medicine": "5d6a3f2d8084eca07d44a24c51cad2b30d509f24d7637a3dfc498eb4130d9ab4", | |
| "mmlu_professional_accounting": "e76d3fecd3a8d85a046320a852baabc77dde6d579180e5951a9a4024efbfe334", | |
| "mmlu_nutrition": "256c3d1d552effcd33f50be2f2334475c3475285ded2a4aa547bdcae91d9606e", | |
| "mmlu_miscellaneous": "eca7ddbf1b63d2895a80dfc1cb8ded47145301a99aae70cb9dc16d2d659750a6", | |
| "mmlu_medical_genetics": "592f41c97533fd402436827e0132723c89981473773a55c83598881b4f470410", | |
| "mmlu_marketing": "df0c07649d9464924de266f377c064a8785b68b036704345616cdb6faa1da90a", | |
| "mmlu_management": "7ad57aca67c8275517539f2a2eab736254e3b869ddf15fb51c124dcc8f2a8c42", | |
| "mmlu_human_aging": "13c88465f0fa28f0b8ba58b86fe8f473e076b400cdb3e70ae3a18c24d7285e86", | |
| "mmlu_global_facts": "2738dead1c31e78dd8c7a4fe36a0ea59bf2ddc3473286aca6fc43b5a0f434937", | |
| "mmlu_college_medicine": "fa1341b6e1f9456f03e3fcf2451032ce061f6d7a9f33c294559dac692a831b83", | |
| "mmlu_clinical_knowledge": "96050a973a085cf9f0c359fd0a86d52c24cd8de3f4ed8d45af55ea34916c6295", | |
| "mmlu_business_ethics": "74a338a2fa19d75e0db5c9a8239b2694e48dbc0ca4e17b84b1724694f18039e1", | |
| "mmlu_machine_learning": "6593736e04ea3fbbe186c0a0ad5c109bb113b625813d8295200143537481da4c", | |
| "mmlu_high_school_statistics": "715ee07b728b5e2c07ffefd41e3ddb1aa54eb2ff82af013bb6a35e050205f902", | |
| "mmlu_high_school_physics": "dabed2206f64c5f75e460c7805e8e070e31bd3640a187ba90bcc9936db5bdbbe", | |
| "mmlu_high_school_mathematics": "f134bdc11447343bc21e0e7d55bdeddaa0d0366a1713b74c56bd88035f5c29f6", | |
| "mmlu_high_school_computer_science": "2afd87dd87ff1011790077973fcdcbf7369cfca0b669982fed948464bbfcd390", | |
| "mmlu_high_school_chemistry": "5e88b5c5973d98b1ed1453c644d8f8c445b59a2e2d3241b8ac4b6961fbad178a", | |
| "mmlu_high_school_biology": "631bd3441fbb05b39cf8f6828db528327fc5dbaa590b48aa62c9188ceb73fb12", | |
| "mmlu_elementary_mathematics": "043a8d7d594442491f74a14550d8450c8ecaac4e915a714cb4cdcfa92eb40dd9", | |
| "mmlu_electrical_engineering": "74d5acdeedcce986f51ce5193913fb04d74e7e421ee07da8cde840daf28a6567", | |
| "mmlu_conceptual_physics": "11b99353fb58e9177f16e1d883726a1c80571823544dd472f713c9ee2c1b7146", | |
| "mmlu_computer_security": "b800488b21b7088f1dd488fc0e6069bd65290c0c6bda5b4ffeb69743f0dc6351", | |
| "mmlu_college_physics": "c8848185bf343dff77cbdd990c8ba9f054bbbcc2c990713100be11974f8d87f1", | |
| "mmlu_college_mathematics": "0305683c1eea6eac232e21b67066ecaf59022e524fe152d5ebbbd22735387295", | |
| "mmlu_college_computer_science": "b5b23960506e7636df03e116275386ed4ea8613aa3e745038a6e3e893579302c", | |
| "mmlu_college_chemistry": "a5e8686c7e4933b8c9aaefd92b694741f8a9f499bf47f9fe774fc7fbc6c39b25", | |
| "mmlu_college_biology": "341d1e5c8dbf2e77290ce7e648684c93cb413ca663c598873e217ba94c17bb6e", | |
| "mmlu_astronomy": "2cbf4dead85c3f9289b4e23559bc6605605091c336fb8c604faac955e3cecc10", | |
| "mmlu_anatomy": "8501d28b8b5db66cb3de56a4f91d76c476d209963035b27f300b7eb56582a5a5", | |
| "mmlu_abstract_algebra": "6884834550c2df63906bce80d69842feb195cb2c94b0058e3e430815064f2e84", | |
| "hellaswag": "08367dc6b0bcc499a61ceb5e2bb309faac370773d411157e2b41fa1cd0071685", | |
| "gsm8k": "7efa95f368d94565b0482934b70414247a72f7e0a266831c6f230e458ae4d5f8", | |
| "arc_easy": "4b18212f34af79189d925d622d59d63af163d437ee385124447e5df2a4c0cc76", | |
| "arc_challenge": "702282f5b493b78739990deb8db08d23b9bc8886634ceebf731b44ddaee21855" | |
| }, | |
| "model_source": "hf", | |
| "model_name": "prince-canuma/Llama-3-6B-v0", | |
| "model_name_sanitized": "prince-canuma__Llama-3-6B-v0", | |
| "start_time": 6671854.743933531, | |
| "end_time": 6672883.977912472, | |
| "total_evaluation_time_seconds": "1029.233978940174" | |
| } |