| { | |
| "results": { | |
| "openllm": { | |
| " ": " ", | |
| "alias": "Open LLM Leaderboard" | |
| }, | |
| "arc_challenge": { | |
| "alias": " - arc_challenge", | |
| "acc,none": 0.6126279863481229, | |
| "acc_stderr,none": 0.01423587248790987, | |
| "acc_norm,none": 0.64419795221843, | |
| "acc_norm_stderr,none": 0.013990571137918755 | |
| }, | |
| "gsm8k": { | |
| "alias": " - gsm8k", | |
| "exact_match,strict-match": 0.48142532221379836, | |
| "exact_match_stderr,strict-match": 0.013762977910317581, | |
| "exact_match,flexible-extract": 0.48597422289613346, | |
| "exact_match_stderr,flexible-extract": 0.013767064940239304 | |
| }, | |
| "hellaswag": { | |
| "alias": " - hellaswag", | |
| "acc,none": 0.6558454491137223, | |
| "acc_stderr,none": 0.0047412082290928656, | |
| "acc_norm,none": 0.8466440948018323, | |
| "acc_norm_stderr,none": 0.003595938124166246 | |
| }, | |
| "mmlu": { | |
| "acc,none": 0.6152969662441248, | |
| "acc_stderr,none": 0.0038963608519415305, | |
| "alias": " - mmlu" | |
| }, | |
| "mmlu_humanities": { | |
| "acc,none": 0.5825717321997874, | |
| "acc_stderr,none": 0.00684899522722564, | |
| "alias": " - humanities" | |
| }, | |
| "mmlu_formal_logic": { | |
| "alias": " - formal_logic", | |
| "acc,none": 0.3968253968253968, | |
| "acc_stderr,none": 0.04375888492727061 | |
| }, | |
| "mmlu_high_school_european_history": { | |
| "alias": " - high_school_european_history", | |
| "acc,none": 0.7575757575757576, | |
| "acc_stderr,none": 0.03346409881055953 | |
| }, | |
| "mmlu_high_school_us_history": { | |
| "alias": " - high_school_us_history", | |
| "acc,none": 0.8088235294117647, | |
| "acc_stderr,none": 0.027599174300640777 | |
| }, | |
| "mmlu_high_school_world_history": { | |
| "alias": " - high_school_world_history", | |
| "acc,none": 0.7890295358649789, | |
| "acc_stderr,none": 0.02655837250266192 | |
| }, | |
| "mmlu_international_law": { | |
| "alias": " - international_law", | |
| "acc,none": 0.768595041322314, | |
| "acc_stderr,none": 0.038498560987940904 | |
| }, | |
| "mmlu_jurisprudence": { | |
| "alias": " - jurisprudence", | |
| "acc,none": 0.75, | |
| "acc_stderr,none": 0.04186091791394607 | |
| }, | |
| "mmlu_logical_fallacies": { | |
| "alias": " - logical_fallacies", | |
| "acc,none": 0.754601226993865, | |
| "acc_stderr,none": 0.03380939813943354 | |
| }, | |
| "mmlu_moral_disputes": { | |
| "alias": " - moral_disputes", | |
| "acc,none": 0.7023121387283237, | |
| "acc_stderr,none": 0.024617055388677 | |
| }, | |
| "mmlu_moral_scenarios": { | |
| "alias": " - moral_scenarios", | |
| "acc,none": 0.4324022346368715, | |
| "acc_stderr,none": 0.01656897123354861 | |
| }, | |
| "mmlu_philosophy": { | |
| "alias": " - philosophy", | |
| "acc,none": 0.684887459807074, | |
| "acc_stderr,none": 0.026385273703464496 | |
| }, | |
| "mmlu_prehistory": { | |
| "alias": " - prehistory", | |
| "acc,none": 0.7098765432098766, | |
| "acc_stderr,none": 0.025251173936495015 | |
| }, | |
| "mmlu_professional_law": { | |
| "alias": " - professional_law", | |
| "acc,none": 0.4589308996088657, | |
| "acc_stderr,none": 0.012727084826799797 | |
| }, | |
| "mmlu_world_religions": { | |
| "alias": " - world_religions", | |
| "acc,none": 0.8187134502923976, | |
| "acc_stderr,none": 0.029547741687640038 | |
| }, | |
| "mmlu_other": { | |
| "acc,none": 0.6816865143224976, | |
| "acc_stderr,none": 0.0081108890838561, | |
| "alias": " - other" | |
| }, | |
| "mmlu_business_ethics": { | |
| "alias": " - business_ethics", | |
| "acc,none": 0.58, | |
| "acc_stderr,none": 0.049604496374885836 | |
| }, | |
| "mmlu_clinical_knowledge": { | |
| "alias": " - clinical_knowledge", | |
| "acc,none": 0.6867924528301886, | |
| "acc_stderr,none": 0.028544793319055326 | |
| }, | |
| "mmlu_college_medicine": { | |
| "alias": " - college_medicine", | |
| "acc,none": 0.6242774566473989, | |
| "acc_stderr,none": 0.03692820767264867 | |
| }, | |
| "mmlu_global_facts": { | |
| "alias": " - global_facts", | |
| "acc,none": 0.39, | |
| "acc_stderr,none": 0.04902071300001975 | |
| }, | |
| "mmlu_human_aging": { | |
| "alias": " - human_aging", | |
| "acc,none": 0.672645739910314, | |
| "acc_stderr,none": 0.03149384670994131 | |
| }, | |
| "mmlu_management": { | |
| "alias": " - management", | |
| "acc,none": 0.7766990291262136, | |
| "acc_stderr,none": 0.04123553189891431 | |
| }, | |
| "mmlu_marketing": { | |
| "alias": " - marketing", | |
| "acc,none": 0.8504273504273504, | |
| "acc_stderr,none": 0.023365051491753715 | |
| }, | |
| "mmlu_medical_genetics": { | |
| "alias": " - medical_genetics", | |
| "acc,none": 0.72, | |
| "acc_stderr,none": 0.045126085985421276 | |
| }, | |
| "mmlu_miscellaneous": { | |
| "alias": " - miscellaneous", | |
| "acc,none": 0.789272030651341, | |
| "acc_stderr,none": 0.014583812465862546 | |
| }, | |
| "mmlu_nutrition": { | |
| "alias": " - nutrition", | |
| "acc,none": 0.6895424836601307, | |
| "acc_stderr,none": 0.026493033225145905 | |
| }, | |
| "mmlu_professional_accounting": { | |
| "alias": " - professional_accounting", | |
| "acc,none": 0.4929078014184397, | |
| "acc_stderr,none": 0.02982449855912901 | |
| }, | |
| "mmlu_professional_medicine": { | |
| "alias": " - professional_medicine", | |
| "acc,none": 0.6470588235294118, | |
| "acc_stderr,none": 0.029029422815681404 | |
| }, | |
| "mmlu_virology": { | |
| "alias": " - virology", | |
| "acc,none": 0.5180722891566265, | |
| "acc_stderr,none": 0.03889951252827217 | |
| }, | |
| "mmlu_social_sciences": { | |
| "acc,none": 0.715307117322067, | |
| "acc_stderr,none": 0.00796871934369757, | |
| "alias": " - social sciences" | |
| }, | |
| "mmlu_econometrics": { | |
| "alias": " - econometrics", | |
| "acc,none": 0.4649122807017544, | |
| "acc_stderr,none": 0.046920083813689104 | |
| }, | |
| "mmlu_high_school_geography": { | |
| "alias": " - high_school_geography", | |
| "acc,none": 0.7777777777777778, | |
| "acc_stderr,none": 0.029620227874790465 | |
| }, | |
| "mmlu_high_school_government_and_politics": { | |
| "alias": " - high_school_government_and_politics", | |
| "acc,none": 0.8756476683937824, | |
| "acc_stderr,none": 0.023814477086593563 | |
| }, | |
| "mmlu_high_school_macroeconomics": { | |
| "alias": " - high_school_macroeconomics", | |
| "acc,none": 0.6153846153846154, | |
| "acc_stderr,none": 0.024666744915187222 | |
| }, | |
| "mmlu_high_school_microeconomics": { | |
| "alias": " - high_school_microeconomics", | |
| "acc,none": 0.6092436974789915, | |
| "acc_stderr,none": 0.031693802357129965 | |
| }, | |
| "mmlu_high_school_psychology": { | |
| "alias": " - high_school_psychology", | |
| "acc,none": 0.8055045871559633, | |
| "acc_stderr,none": 0.01697028909045807 | |
| }, | |
| "mmlu_human_sexuality": { | |
| "alias": " - human_sexuality", | |
| "acc,none": 0.7786259541984732, | |
| "acc_stderr,none": 0.03641297081313729 | |
| }, | |
| "mmlu_professional_psychology": { | |
| "alias": " - professional_psychology", | |
| "acc,none": 0.6748366013071896, | |
| "acc_stderr,none": 0.018950886770806304 | |
| }, | |
| "mmlu_public_relations": { | |
| "alias": " - public_relations", | |
| "acc,none": 0.6909090909090909, | |
| "acc_stderr,none": 0.044262946482000985 | |
| }, | |
| "mmlu_security_studies": { | |
| "alias": " - security_studies", | |
| "acc,none": 0.6979591836734694, | |
| "acc_stderr,none": 0.0293936093198798 | |
| }, | |
| "mmlu_sociology": { | |
| "alias": " - sociology", | |
| "acc,none": 0.7711442786069652, | |
| "acc_stderr,none": 0.029705284056772422 | |
| }, | |
| "mmlu_us_foreign_policy": { | |
| "alias": " - us_foreign_policy", | |
| "acc,none": 0.84, | |
| "acc_stderr,none": 0.03684529491774708 | |
| }, | |
| "mmlu_stem": { | |
| "acc,none": 0.5011100539169046, | |
| "acc_stderr,none": 0.008503308501443964, | |
| "alias": " - stem" | |
| }, | |
| "mmlu_abstract_algebra": { | |
| "alias": " - abstract_algebra", | |
| "acc,none": 0.3, | |
| "acc_stderr,none": 0.046056618647183814 | |
| }, | |
| "mmlu_anatomy": { | |
| "alias": " - anatomy", | |
| "acc,none": 0.6370370370370371, | |
| "acc_stderr,none": 0.04153948404742398 | |
| }, | |
| "mmlu_astronomy": { | |
| "alias": " - astronomy", | |
| "acc,none": 0.6644736842105263, | |
| "acc_stderr,none": 0.03842498559395269 | |
| }, | |
| "mmlu_college_biology": { | |
| "alias": " - college_biology", | |
| "acc,none": 0.7152777777777778, | |
| "acc_stderr,none": 0.03773809990686935 | |
| }, | |
| "mmlu_college_chemistry": { | |
| "alias": " - college_chemistry", | |
| "acc,none": 0.43, | |
| "acc_stderr,none": 0.049756985195624284 | |
| }, | |
| "mmlu_college_computer_science": { | |
| "alias": " - college_computer_science", | |
| "acc,none": 0.44, | |
| "acc_stderr,none": 0.04988876515698589 | |
| }, | |
| "mmlu_college_mathematics": { | |
| "alias": " - college_mathematics", | |
| "acc,none": 0.3, | |
| "acc_stderr,none": 0.046056618647183814 | |
| }, | |
| "mmlu_college_physics": { | |
| "alias": " - college_physics", | |
| "acc,none": 0.3235294117647059, | |
| "acc_stderr,none": 0.046550104113196177 | |
| }, | |
| "mmlu_computer_security": { | |
| "alias": " - computer_security", | |
| "acc,none": 0.72, | |
| "acc_stderr,none": 0.04512608598542128 | |
| }, | |
| "mmlu_conceptual_physics": { | |
| "alias": " - conceptual_physics", | |
| "acc,none": 0.5191489361702127, | |
| "acc_stderr,none": 0.03266204299064678 | |
| }, | |
| "mmlu_electrical_engineering": { | |
| "alias": " - electrical_engineering", | |
| "acc,none": 0.5655172413793104, | |
| "acc_stderr,none": 0.04130740879555498 | |
| }, | |
| "mmlu_elementary_mathematics": { | |
| "alias": " - elementary_mathematics", | |
| "acc,none": 0.41005291005291006, | |
| "acc_stderr,none": 0.025331202438944433 | |
| }, | |
| "mmlu_high_school_biology": { | |
| "alias": " - high_school_biology", | |
| "acc,none": 0.7516129032258064, | |
| "acc_stderr,none": 0.024580028921481003 | |
| }, | |
| "mmlu_high_school_chemistry": { | |
| "alias": " - high_school_chemistry", | |
| "acc,none": 0.4827586206896552, | |
| "acc_stderr,none": 0.035158955511657 | |
| }, | |
| "mmlu_high_school_computer_science": { | |
| "alias": " - high_school_computer_science", | |
| "acc,none": 0.67, | |
| "acc_stderr,none": 0.04725815626252607 | |
| }, | |
| "mmlu_high_school_mathematics": { | |
| "alias": " - high_school_mathematics", | |
| "acc,none": 0.2962962962962963, | |
| "acc_stderr,none": 0.027840811495871927 | |
| }, | |
| "mmlu_high_school_physics": { | |
| "alias": " - high_school_physics", | |
| "acc,none": 0.304635761589404, | |
| "acc_stderr,none": 0.03757949922943342 | |
| }, | |
| "mmlu_high_school_statistics": { | |
| "alias": " - high_school_statistics", | |
| "acc,none": 0.47685185185185186, | |
| "acc_stderr,none": 0.034063153607115065 | |
| }, | |
| "mmlu_machine_learning": { | |
| "alias": " - machine_learning", | |
| "acc,none": 0.4642857142857143, | |
| "acc_stderr,none": 0.04733667890053756 | |
| }, | |
| "truthfulqa_gen": { | |
| "alias": " - truthfulqa_gen", | |
| "bleu_max,none": 25.700033691737175, | |
| "bleu_max_stderr,none": 0.7881599796929746, | |
| "bleu_acc,none": 0.5275397796817626, | |
| "bleu_acc_stderr,none": 0.01747693019071219, | |
| "bleu_diff,none": 5.3040911408382, | |
| "bleu_diff_stderr,none": 0.8019018732185539, | |
| "rouge1_max,none": 52.53999488184873, | |
| "rouge1_max_stderr,none": 0.8450489572832366, | |
| "rouge1_acc,none": 0.543451652386781, | |
| "rouge1_acc_stderr,none": 0.0174372809531837, | |
| "rouge1_diff,none": 7.762455581676218, | |
| "rouge1_diff_stderr,none": 1.0899538384131906, | |
| "rouge2_max,none": 38.4236012938653, | |
| "rouge2_max_stderr,none": 0.9912380108121523, | |
| "rouge2_acc,none": 0.48714810281517745, | |
| "rouge2_acc_stderr,none": 0.017497717944299808, | |
| "rouge2_diff,none": 7.53452833652938, | |
| "rouge2_diff_stderr,none": 1.1744005464598066, | |
| "rougeL_max,none": 49.22977101366606, | |
| "rougeL_max_stderr,none": 0.8692060028738016, | |
| "rougeL_acc,none": 0.5422276621787026, | |
| "rougeL_acc_stderr,none": 0.01744096571248212, | |
| "rougeL_diff,none": 7.610158522282575, | |
| "rougeL_diff_stderr,none": 1.097679115753027 | |
| }, | |
| "truthfulqa_mc1": { | |
| "alias": " - truthfulqa_mc1", | |
| "acc,none": 0.40636474908200737, | |
| "acc_stderr,none": 0.017193835812093886 | |
| }, | |
| "truthfulqa_mc2": { | |
| "alias": " - truthfulqa_mc2", | |
| "acc,none": 0.5830102420331416, | |
| "acc_stderr,none": 0.015649169405019345 | |
| }, | |
| "winogrande": { | |
| "alias": " - winogrande", | |
| "acc,none": 0.7868981846882399, | |
| "acc_stderr,none": 0.011508957690722747 | |
| } | |
| }, | |
| "groups": { | |
| "mmlu": { | |
| "acc,none": 0.6152969662441248, | |
| "acc_stderr,none": 0.0038963608519415305, | |
| "alias": " - mmlu" | |
| }, | |
| "mmlu_humanities": { | |
| "acc,none": 0.5825717321997874, | |
| "acc_stderr,none": 0.00684899522722564, | |
| "alias": " - humanities" | |
| }, | |
| "mmlu_other": { | |
| "acc,none": 0.6816865143224976, | |
| "acc_stderr,none": 0.0081108890838561, | |
| "alias": " - other" | |
| }, | |
| "mmlu_social_sciences": { | |
| "acc,none": 0.715307117322067, | |
| "acc_stderr,none": 0.00796871934369757, | |
| "alias": " - social sciences" | |
| }, | |
| "mmlu_stem": { | |
| "acc,none": 0.5011100539169046, | |
| "acc_stderr,none": 0.008503308501443964, | |
| "alias": " - stem" | |
| } | |
| }, | |
| "group_subtasks": { | |
| "mmlu_humanities": [ | |
| "mmlu_prehistory", | |
| "mmlu_moral_scenarios", | |
| "mmlu_moral_disputes", | |
| "mmlu_philosophy", | |
| "mmlu_high_school_world_history", | |
| "mmlu_logical_fallacies", | |
| "mmlu_formal_logic", | |
| "mmlu_high_school_us_history", | |
| "mmlu_high_school_european_history", | |
| "mmlu_professional_law", | |
| "mmlu_jurisprudence", | |
| "mmlu_international_law", | |
| "mmlu_world_religions" | |
| ], | |
| "mmlu_social_sciences": [ | |
| "mmlu_high_school_psychology", | |
| "mmlu_us_foreign_policy", | |
| "mmlu_professional_psychology", | |
| "mmlu_human_sexuality", | |
| "mmlu_public_relations", | |
| "mmlu_security_studies", | |
| "mmlu_sociology", | |
| "mmlu_high_school_microeconomics", | |
| "mmlu_high_school_macroeconomics", | |
| "mmlu_high_school_government_and_politics", | |
| "mmlu_econometrics", | |
| "mmlu_high_school_geography" | |
| ], | |
| "mmlu_other": [ | |
| "mmlu_professional_accounting", | |
| "mmlu_global_facts", | |
| "mmlu_college_medicine", | |
| "mmlu_virology", | |
| "mmlu_human_aging", | |
| "mmlu_professional_medicine", | |
| "mmlu_medical_genetics", | |
| "mmlu_business_ethics", | |
| "mmlu_management", | |
| "mmlu_miscellaneous", | |
| "mmlu_nutrition", | |
| "mmlu_marketing", | |
| "mmlu_clinical_knowledge" | |
| ], | |
| "mmlu_stem": [ | |
| "mmlu_anatomy", | |
| "mmlu_college_physics", | |
| "mmlu_computer_security", | |
| "mmlu_conceptual_physics", | |
| "mmlu_high_school_chemistry", | |
| "mmlu_college_computer_science", | |
| "mmlu_high_school_biology", | |
| "mmlu_high_school_mathematics", | |
| "mmlu_high_school_statistics", | |
| "mmlu_college_chemistry", | |
| "mmlu_electrical_engineering", | |
| "mmlu_college_mathematics", | |
| "mmlu_college_biology", | |
| "mmlu_abstract_algebra", | |
| "mmlu_elementary_mathematics", | |
| "mmlu_high_school_physics", | |
| "mmlu_high_school_computer_science", | |
| "mmlu_machine_learning", | |
| "mmlu_astronomy" | |
| ], | |
| "mmlu": [ | |
| "mmlu_stem", | |
| "mmlu_other", | |
| "mmlu_social_sciences", | |
| "mmlu_humanities" | |
| ], | |
| "openllm": [ | |
| "arc_challenge", | |
| "hellaswag", | |
| "truthfulqa_mc2", | |
| "truthfulqa_mc1", | |
| "truthfulqa_gen", | |
| "mmlu", | |
| "winogrande", | |
| "gsm8k" | |
| ] | |
| }, | |
| "configs": { | |
| "arc_challenge": { | |
| "task": "arc_challenge", | |
| "tag": [ | |
| "ai2_arc" | |
| ], | |
| "dataset_path": "allenai/ai2_arc", | |
| "dataset_name": "ARC-Challenge", | |
| "training_split": "train", | |
| "validation_split": "validation", | |
| "test_split": "test", | |
| "fewshot_split": "validation", | |
| "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": 25, | |
| "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", | |
| "tag": [ | |
| "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", | |
| "tag": [ | |
| "multiple_choice" | |
| ], | |
| "dataset_path": "hellaswag", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "training_split": "train", | |
| "validation_split": "validation", | |
| "fewshot_split": "train", | |
| "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": 10, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "abstract_algebra", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "anatomy", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "astronomy", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "business_ethics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "clinical_knowledge", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_biology", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_chemistry", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_computer_science", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_mathematics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_medicine", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "college_physics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "computer_security", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "conceptual_physics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "econometrics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "electrical_engineering", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "elementary_mathematics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "formal_logic", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "global_facts", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_biology", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_chemistry", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_computer_science", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_european_history", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_geography", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_government_and_politics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_macroeconomics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_mathematics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_microeconomics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_physics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_psychology", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_statistics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_us_history", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "high_school_world_history", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "human_aging", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "human_sexuality", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "international_law", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "jurisprudence", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "logical_fallacies", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_stem_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "machine_learning", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "management", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "marketing", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "medical_genetics", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "miscellaneous", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "moral_disputes", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "moral_scenarios", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "nutrition", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "philosophy", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "prehistory", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "professional_accounting", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "professional_law", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "professional_medicine", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "professional_psychology", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "public_relations", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "security_studies", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "sociology", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_social_sciences_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "us_foreign_policy", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_other_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "virology", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": "mmlu_humanities_tasks", | |
| "dataset_path": "hails/mmlu_no_train", | |
| "dataset_name": "world_religions", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "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": 5, | |
| "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", | |
| "tag": [ | |
| "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", | |
| "tag": [ | |
| "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", | |
| "tag": [ | |
| "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", | |
| "dataset_kwargs": { | |
| "trust_remote_code": true | |
| }, | |
| "training_split": "train", | |
| "validation_split": "validation", | |
| "fewshot_split": "train", | |
| "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": 5, | |
| "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, | |
| "gsm8k": 3.0, | |
| "hellaswag": 1.0, | |
| "mmlu": 1, | |
| "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_humanities": 1, | |
| "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_other": 1, | |
| "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_social_sciences": 1, | |
| "mmlu_sociology": 0.0, | |
| "mmlu_stem": 1, | |
| "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": { | |
| "arc_challenge": 25, | |
| "gsm8k": 5, | |
| "hellaswag": 10, | |
| "mmlu_abstract_algebra": 5, | |
| "mmlu_anatomy": 5, | |
| "mmlu_astronomy": 5, | |
| "mmlu_business_ethics": 5, | |
| "mmlu_clinical_knowledge": 5, | |
| "mmlu_college_biology": 5, | |
| "mmlu_college_chemistry": 5, | |
| "mmlu_college_computer_science": 5, | |
| "mmlu_college_mathematics": 5, | |
| "mmlu_college_medicine": 5, | |
| "mmlu_college_physics": 5, | |
| "mmlu_computer_security": 5, | |
| "mmlu_conceptual_physics": 5, | |
| "mmlu_econometrics": 5, | |
| "mmlu_electrical_engineering": 5, | |
| "mmlu_elementary_mathematics": 5, | |
| "mmlu_formal_logic": 5, | |
| "mmlu_global_facts": 5, | |
| "mmlu_high_school_biology": 5, | |
| "mmlu_high_school_chemistry": 5, | |
| "mmlu_high_school_computer_science": 5, | |
| "mmlu_high_school_european_history": 5, | |
| "mmlu_high_school_geography": 5, | |
| "mmlu_high_school_government_and_politics": 5, | |
| "mmlu_high_school_macroeconomics": 5, | |
| "mmlu_high_school_mathematics": 5, | |
| "mmlu_high_school_microeconomics": 5, | |
| "mmlu_high_school_physics": 5, | |
| "mmlu_high_school_psychology": 5, | |
| "mmlu_high_school_statistics": 5, | |
| "mmlu_high_school_us_history": 5, | |
| "mmlu_high_school_world_history": 5, | |
| "mmlu_human_aging": 5, | |
| "mmlu_human_sexuality": 5, | |
| "mmlu_international_law": 5, | |
| "mmlu_jurisprudence": 5, | |
| "mmlu_logical_fallacies": 5, | |
| "mmlu_machine_learning": 5, | |
| "mmlu_management": 5, | |
| "mmlu_marketing": 5, | |
| "mmlu_medical_genetics": 5, | |
| "mmlu_miscellaneous": 5, | |
| "mmlu_moral_disputes": 5, | |
| "mmlu_moral_scenarios": 5, | |
| "mmlu_nutrition": 5, | |
| "mmlu_philosophy": 5, | |
| "mmlu_prehistory": 5, | |
| "mmlu_professional_accounting": 5, | |
| "mmlu_professional_law": 5, | |
| "mmlu_professional_medicine": 5, | |
| "mmlu_professional_psychology": 5, | |
| "mmlu_public_relations": 5, | |
| "mmlu_security_studies": 5, | |
| "mmlu_sociology": 5, | |
| "mmlu_us_foreign_policy": 5, | |
| "mmlu_virology": 5, | |
| "mmlu_world_religions": 5, | |
| "truthfulqa_gen": 0, | |
| "truthfulqa_mc1": 0, | |
| "truthfulqa_mc2": 0, | |
| "winogrande": 5 | |
| }, | |
| "higher_is_better": { | |
| "arc_challenge": { | |
| "acc": true, | |
| "acc_norm": true | |
| }, | |
| "gsm8k": { | |
| "exact_match": true | |
| }, | |
| "hellaswag": { | |
| "acc": true, | |
| "acc_norm": true | |
| }, | |
| "mmlu": { | |
| "acc": true, | |
| "acc_norm": true, | |
| "bleu_max": true, | |
| "bleu_acc": true, | |
| "bleu_diff": true, | |
| "rouge1_max": true, | |
| "rouge1_acc": true, | |
| "rouge1_diff": true, | |
| "rouge2_max": true, | |
| "rouge2_acc": true, | |
| "rouge2_diff": true, | |
| "rougeL_max": true, | |
| "rougeL_acc": true, | |
| "rougeL_diff": true, | |
| "exact_match": true | |
| }, | |
| "mmlu_abstract_algebra": { | |
| "acc": true | |
| }, | |
| "mmlu_anatomy": { | |
| "acc": true | |
| }, | |
| "mmlu_astronomy": { | |
| "acc": true | |
| }, | |
| "mmlu_business_ethics": { | |
| "acc": true | |
| }, | |
| "mmlu_clinical_knowledge": { | |
| "acc": true | |
| }, | |
| "mmlu_college_biology": { | |
| "acc": true | |
| }, | |
| "mmlu_college_chemistry": { | |
| "acc": true | |
| }, | |
| "mmlu_college_computer_science": { | |
| "acc": true | |
| }, | |
| "mmlu_college_mathematics": { | |
| "acc": true | |
| }, | |
| "mmlu_college_medicine": { | |
| "acc": true | |
| }, | |
| "mmlu_college_physics": { | |
| "acc": true | |
| }, | |
| "mmlu_computer_security": { | |
| "acc": true | |
| }, | |
| "mmlu_conceptual_physics": { | |
| "acc": true | |
| }, | |
| "mmlu_econometrics": { | |
| "acc": true | |
| }, | |
| "mmlu_electrical_engineering": { | |
| "acc": true | |
| }, | |
| "mmlu_elementary_mathematics": { | |
| "acc": true | |
| }, | |
| "mmlu_formal_logic": { | |
| "acc": true | |
| }, | |
| "mmlu_global_facts": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_biology": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_chemistry": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_computer_science": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_european_history": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_geography": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_government_and_politics": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_macroeconomics": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_mathematics": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_microeconomics": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_physics": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_psychology": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_statistics": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_us_history": { | |
| "acc": true | |
| }, | |
| "mmlu_high_school_world_history": { | |
| "acc": true | |
| }, | |
| "mmlu_human_aging": { | |
| "acc": true | |
| }, | |
| "mmlu_human_sexuality": { | |
| "acc": true | |
| }, | |
| "mmlu_humanities": { | |
| "acc": true, | |
| "acc_norm": true, | |
| "bleu_max": true, | |
| "bleu_acc": true, | |
| "bleu_diff": true, | |
| "rouge1_max": true, | |
| "rouge1_acc": true, | |
| "rouge1_diff": true, | |
| "rouge2_max": true, | |
| "rouge2_acc": true, | |
| "rouge2_diff": true, | |
| "rougeL_max": true, | |
| "rougeL_acc": true, | |
| "rougeL_diff": true, | |
| "exact_match": true | |
| }, | |
| "mmlu_international_law": { | |
| "acc": true | |
| }, | |
| "mmlu_jurisprudence": { | |
| "acc": true | |
| }, | |
| "mmlu_logical_fallacies": { | |
| "acc": true | |
| }, | |
| "mmlu_machine_learning": { | |
| "acc": true | |
| }, | |
| "mmlu_management": { | |
| "acc": true | |
| }, | |
| "mmlu_marketing": { | |
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| "total_evaluation_time_seconds": "10609.87472771667" | |
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