| { | |
| "results": { | |
| "openllm": { | |
| " ": " ", | |
| "alias": "Open LLM Leaderboard" | |
| }, | |
| "arc_challenge": { | |
| "alias": " - arc_challenge", | |
| "acc,none": 0.5955631399317406, | |
| "acc_stderr,none": 0.01434203648343618, | |
| "acc_norm,none": 0.6262798634812287, | |
| "acc_norm_stderr,none": 0.01413770860175908 | |
| }, | |
| "gsm8k": { | |
| "alias": " - gsm8k", | |
| "exact_match,strict-match": 0.5989385898407885, | |
| "exact_match_stderr,strict-match": 0.013500158922245544, | |
| "exact_match,flexible-extract": 0.6990144048521607, | |
| "exact_match_stderr,flexible-extract": 0.012634504465211183 | |
| }, | |
| "hellaswag": { | |
| "alias": " - hellaswag", | |
| "acc,none": 0.609838677554272, | |
| "acc_stderr,none": 0.0048678939272581625, | |
| "acc_norm,none": 0.8123879705238001, | |
| "acc_norm_stderr,none": 0.0038960417231669268 | |
| }, | |
| "mmlu": { | |
| "acc,none": 0.7031049708018801, | |
| "acc_stderr,none": 0.0036674176940399285, | |
| "alias": " - mmlu" | |
| }, | |
| "mmlu_humanities": { | |
| "acc,none": 0.6446333687566419, | |
| "acc_stderr,none": 0.006628123312720251, | |
| "alias": " - humanities" | |
| }, | |
| "mmlu_formal_logic": { | |
| "alias": " - formal_logic", | |
| "acc,none": 0.5396825396825397, | |
| "acc_stderr,none": 0.04458029125470973 | |
| }, | |
| "mmlu_high_school_european_history": { | |
| "alias": " - high_school_european_history", | |
| "acc,none": 0.8121212121212121, | |
| "acc_stderr,none": 0.03050193405942914 | |
| }, | |
| "mmlu_high_school_us_history": { | |
| "alias": " - high_school_us_history", | |
| "acc,none": 0.8431372549019608, | |
| "acc_stderr,none": 0.025524722324553335 | |
| }, | |
| "mmlu_high_school_world_history": { | |
| "alias": " - high_school_world_history", | |
| "acc,none": 0.8607594936708861, | |
| "acc_stderr,none": 0.022535526352692712 | |
| }, | |
| "mmlu_international_law": { | |
| "alias": " - international_law", | |
| "acc,none": 0.8347107438016529, | |
| "acc_stderr,none": 0.03390780612972776 | |
| }, | |
| "mmlu_jurisprudence": { | |
| "alias": " - jurisprudence", | |
| "acc,none": 0.8333333333333334, | |
| "acc_stderr,none": 0.03602814176392645 | |
| }, | |
| "mmlu_logical_fallacies": { | |
| "alias": " - logical_fallacies", | |
| "acc,none": 0.7975460122699386, | |
| "acc_stderr,none": 0.031570650789119005 | |
| }, | |
| "mmlu_moral_disputes": { | |
| "alias": " - moral_disputes", | |
| "acc,none": 0.7745664739884393, | |
| "acc_stderr,none": 0.022497230190967558 | |
| }, | |
| "mmlu_moral_scenarios": { | |
| "alias": " - moral_scenarios", | |
| "acc,none": 0.47262569832402235, | |
| "acc_stderr,none": 0.016697420650642752 | |
| }, | |
| "mmlu_philosophy": { | |
| "alias": " - philosophy", | |
| "acc,none": 0.7781350482315113, | |
| "acc_stderr,none": 0.02359885829286305 | |
| }, | |
| "mmlu_prehistory": { | |
| "alias": " - prehistory", | |
| "acc,none": 0.7870370370370371, | |
| "acc_stderr,none": 0.02277971908873339 | |
| }, | |
| "mmlu_professional_law": { | |
| "alias": " - professional_law", | |
| "acc,none": 0.5260756192959583, | |
| "acc_stderr,none": 0.012752858346533134 | |
| }, | |
| "mmlu_world_religions": { | |
| "alias": " - world_religions", | |
| "acc,none": 0.8128654970760234, | |
| "acc_stderr,none": 0.029913127232368032 | |
| }, | |
| "mmlu_other": { | |
| "acc,none": 0.7521725136787898, | |
| "acc_stderr,none": 0.007482454564765459, | |
| "alias": " - other" | |
| }, | |
| "mmlu_business_ethics": { | |
| "alias": " - business_ethics", | |
| "acc,none": 0.77, | |
| "acc_stderr,none": 0.04229525846816506 | |
| }, | |
| "mmlu_clinical_knowledge": { | |
| "alias": " - clinical_knowledge", | |
| "acc,none": 0.7584905660377359, | |
| "acc_stderr,none": 0.026341480371118362 | |
| }, | |
| "mmlu_college_medicine": { | |
| "alias": " - college_medicine", | |
| "acc,none": 0.6878612716763006, | |
| "acc_stderr,none": 0.03533133389323657 | |
| }, | |
| "mmlu_global_facts": { | |
| "alias": " - global_facts", | |
| "acc,none": 0.48, | |
| "acc_stderr,none": 0.050211673156867795 | |
| }, | |
| "mmlu_human_aging": { | |
| "alias": " - human_aging", | |
| "acc,none": 0.7488789237668162, | |
| "acc_stderr,none": 0.02910522083322462 | |
| }, | |
| "mmlu_management": { | |
| "alias": " - management", | |
| "acc,none": 0.8058252427184466, | |
| "acc_stderr,none": 0.03916667762822583 | |
| }, | |
| "mmlu_marketing": { | |
| "alias": " - marketing", | |
| "acc,none": 0.905982905982906, | |
| "acc_stderr,none": 0.019119892798924974 | |
| }, | |
| "mmlu_medical_genetics": { | |
| "alias": " - medical_genetics", | |
| "acc,none": 0.81, | |
| "acc_stderr,none": 0.03942772444036623 | |
| }, | |
| "mmlu_miscellaneous": { | |
| "alias": " - miscellaneous", | |
| "acc,none": 0.8544061302681992, | |
| "acc_stderr,none": 0.012612475800423456 | |
| }, | |
| "mmlu_nutrition": { | |
| "alias": " - nutrition", | |
| "acc,none": 0.7875816993464052, | |
| "acc_stderr,none": 0.023420375478296118 | |
| }, | |
| "mmlu_professional_accounting": { | |
| "alias": " - professional_accounting", | |
| "acc,none": 0.5531914893617021, | |
| "acc_stderr,none": 0.0296582350976669 | |
| }, | |
| "mmlu_professional_medicine": { | |
| "alias": " - professional_medicine", | |
| "acc,none": 0.6985294117647058, | |
| "acc_stderr,none": 0.027875982114273168 | |
| }, | |
| "mmlu_virology": { | |
| "alias": " - virology", | |
| "acc,none": 0.5602409638554217, | |
| "acc_stderr,none": 0.03864139923699121 | |
| }, | |
| "mmlu_social_sciences": { | |
| "acc,none": 0.8046798830029249, | |
| "acc_stderr,none": 0.007045647830357574, | |
| "alias": " - social sciences" | |
| }, | |
| "mmlu_econometrics": { | |
| "alias": " - econometrics", | |
| "acc,none": 0.631578947368421, | |
| "acc_stderr,none": 0.04537815354939391 | |
| }, | |
| "mmlu_high_school_geography": { | |
| "alias": " - high_school_geography", | |
| "acc,none": 0.8838383838383839, | |
| "acc_stderr,none": 0.022828881775249377 | |
| }, | |
| "mmlu_high_school_government_and_politics": { | |
| "alias": " - high_school_government_and_politics", | |
| "acc,none": 0.9067357512953368, | |
| "acc_stderr,none": 0.020986854593289705 | |
| }, | |
| "mmlu_high_school_macroeconomics": { | |
| "alias": " - high_school_macroeconomics", | |
| "acc,none": 0.7512820512820513, | |
| "acc_stderr,none": 0.021916957709213796 | |
| }, | |
| "mmlu_high_school_microeconomics": { | |
| "alias": " - high_school_microeconomics", | |
| "acc,none": 0.8277310924369747, | |
| "acc_stderr,none": 0.024528664971305424 | |
| }, | |
| "mmlu_high_school_psychology": { | |
| "alias": " - high_school_psychology", | |
| "acc,none": 0.8733944954128441, | |
| "acc_stderr,none": 0.014257128686165169 | |
| }, | |
| "mmlu_human_sexuality": { | |
| "alias": " - human_sexuality", | |
| "acc,none": 0.8091603053435115, | |
| "acc_stderr,none": 0.03446513350752596 | |
| }, | |
| "mmlu_professional_psychology": { | |
| "alias": " - professional_psychology", | |
| "acc,none": 0.7369281045751634, | |
| "acc_stderr,none": 0.017812676542320657 | |
| }, | |
| "mmlu_public_relations": { | |
| "alias": " - public_relations", | |
| "acc,none": 0.7363636363636363, | |
| "acc_stderr,none": 0.04220224692971987 | |
| }, | |
| "mmlu_security_studies": { | |
| "alias": " - security_studies", | |
| "acc,none": 0.7673469387755102, | |
| "acc_stderr,none": 0.02704925791589618 | |
| }, | |
| "mmlu_sociology": { | |
| "alias": " - sociology", | |
| "acc,none": 0.8706467661691543, | |
| "acc_stderr,none": 0.023729830881018515 | |
| }, | |
| "mmlu_us_foreign_policy": { | |
| "alias": " - us_foreign_policy", | |
| "acc,none": 0.87, | |
| "acc_stderr,none": 0.033799766898963086 | |
| }, | |
| "mmlu_stem": { | |
| "acc,none": 0.6428797970187123, | |
| "acc_stderr,none": 0.008203846896313965, | |
| "alias": " - stem" | |
| }, | |
| "mmlu_abstract_algebra": { | |
| "alias": " - abstract_algebra", | |
| "acc,none": 0.48, | |
| "acc_stderr,none": 0.050211673156867795 | |
| }, | |
| "mmlu_anatomy": { | |
| "alias": " - anatomy", | |
| "acc,none": 0.5925925925925926, | |
| "acc_stderr,none": 0.042446332383532286 | |
| }, | |
| "mmlu_astronomy": { | |
| "alias": " - astronomy", | |
| "acc,none": 0.8026315789473685, | |
| "acc_stderr,none": 0.03238981601699397 | |
| }, | |
| "mmlu_college_biology": { | |
| "alias": " - college_biology", | |
| "acc,none": 0.8125, | |
| "acc_stderr,none": 0.032639560491693344 | |
| }, | |
| "mmlu_college_chemistry": { | |
| "alias": " - college_chemistry", | |
| "acc,none": 0.5, | |
| "acc_stderr,none": 0.050251890762960605 | |
| }, | |
| "mmlu_college_computer_science": { | |
| "alias": " - college_computer_science", | |
| "acc,none": 0.64, | |
| "acc_stderr,none": 0.04824181513244218 | |
| }, | |
| "mmlu_college_mathematics": { | |
| "alias": " - college_mathematics", | |
| "acc,none": 0.38, | |
| "acc_stderr,none": 0.04878317312145633 | |
| }, | |
| "mmlu_college_physics": { | |
| "alias": " - college_physics", | |
| "acc,none": 0.4411764705882353, | |
| "acc_stderr,none": 0.049406356306056595 | |
| }, | |
| "mmlu_computer_security": { | |
| "alias": " - computer_security", | |
| "acc,none": 0.78, | |
| "acc_stderr,none": 0.04163331998932262 | |
| }, | |
| "mmlu_conceptual_physics": { | |
| "alias": " - conceptual_physics", | |
| "acc,none": 0.7191489361702128, | |
| "acc_stderr,none": 0.02937917046412481 | |
| }, | |
| "mmlu_electrical_engineering": { | |
| "alias": " - electrical_engineering", | |
| "acc,none": 0.7586206896551724, | |
| "acc_stderr,none": 0.03565998174135302 | |
| }, | |
| "mmlu_elementary_mathematics": { | |
| "alias": " - elementary_mathematics", | |
| "acc,none": 0.6243386243386243, | |
| "acc_stderr,none": 0.02494236893115978 | |
| }, | |
| "mmlu_high_school_biology": { | |
| "alias": " - high_school_biology", | |
| "acc,none": 0.864516129032258, | |
| "acc_stderr,none": 0.019469334586486902 | |
| }, | |
| "mmlu_high_school_chemistry": { | |
| "alias": " - high_school_chemistry", | |
| "acc,none": 0.6354679802955665, | |
| "acc_stderr,none": 0.0338640574606209 | |
| }, | |
| "mmlu_high_school_computer_science": { | |
| "alias": " - high_school_computer_science", | |
| "acc,none": 0.74, | |
| "acc_stderr,none": 0.0440844002276808 | |
| }, | |
| "mmlu_high_school_mathematics": { | |
| "alias": " - high_school_mathematics", | |
| "acc,none": 0.5074074074074074, | |
| "acc_stderr,none": 0.0304821923951915 | |
| }, | |
| "mmlu_high_school_physics": { | |
| "alias": " - high_school_physics", | |
| "acc,none": 0.4900662251655629, | |
| "acc_stderr,none": 0.04081677107248437 | |
| }, | |
| "mmlu_high_school_statistics": { | |
| "alias": " - high_school_statistics", | |
| "acc,none": 0.6435185185185185, | |
| "acc_stderr,none": 0.032664783315272714 | |
| }, | |
| "mmlu_machine_learning": { | |
| "alias": " - machine_learning", | |
| "acc,none": 0.4375, | |
| "acc_stderr,none": 0.04708567521880525 | |
| }, | |
| "truthfulqa_gen": { | |
| "alias": " - truthfulqa_gen", | |
| "bleu_max,none": 20.093141383074126, | |
| "bleu_max_stderr,none": 0.7674708727449174, | |
| "bleu_acc,none": 0.4981640146878825, | |
| "bleu_acc_stderr,none": 0.017503383046877066, | |
| "bleu_diff,none": 5.514451116317338, | |
| "bleu_diff_stderr,none": 0.707963630379558, | |
| "rouge1_max,none": 43.71945472469494, | |
| "rouge1_max_stderr,none": 0.9285679841034437, | |
| "rouge1_acc,none": 0.5250917992656059, | |
| "rouge1_acc_stderr,none": 0.017481446804103993, | |
| "rouge1_diff,none": 7.180570525369235, | |
| "rouge1_diff_stderr,none": 1.0338082939518565, | |
| "rouge2_max,none": 30.409106400754577, | |
| "rouge2_max_stderr,none": 1.0240150293546637, | |
| "rouge2_acc,none": 0.43451652386780903, | |
| "rouge2_acc_stderr,none": 0.017352738749259557, | |
| "rouge2_diff,none": 7.515663779557191, | |
| "rouge2_diff_stderr,none": 1.1049877019338414, | |
| "rougeL_max,none": 40.779159784653935, | |
| "rougeL_max_stderr,none": 0.9436650478795271, | |
| "rougeL_acc,none": 0.5140758873929009, | |
| "rougeL_acc_stderr,none": 0.017496563717042796, | |
| "rougeL_diff,none": 7.052881861044437, | |
| "rougeL_diff_stderr,none": 1.043772558534271 | |
| }, | |
| "truthfulqa_mc1": { | |
| "alias": " - truthfulqa_mc1", | |
| "acc,none": 0.397796817625459, | |
| "acc_stderr,none": 0.017133934248559655 | |
| }, | |
| "truthfulqa_mc2": { | |
| "alias": " - truthfulqa_mc2", | |
| "acc,none": 0.5691656979068941, | |
| "acc_stderr,none": 0.01543514656981664 | |
| }, | |
| "winogrande": { | |
| "alias": " - winogrande", | |
| "acc,none": 0.7521704814522494, | |
| "acc_stderr,none": 0.012134386019865344 | |
| } | |
| }, | |
| "groups": { | |
| "mmlu": { | |
| "acc,none": 0.7031049708018801, | |
| "acc_stderr,none": 0.0036674176940399285, | |
| "alias": " - mmlu" | |
| }, | |
| "mmlu_humanities": { | |
| "acc,none": 0.6446333687566419, | |
| "acc_stderr,none": 0.006628123312720251, | |
| "alias": " - humanities" | |
| }, | |
| "mmlu_other": { | |
| "acc,none": 0.7521725136787898, | |
| "acc_stderr,none": 0.007482454564765459, | |
| "alias": " - other" | |
| }, | |
| "mmlu_social_sciences": { | |
| "acc,none": 0.8046798830029249, | |
| "acc_stderr,none": 0.007045647830357574, | |
| "alias": " - social sciences" | |
| }, | |
| "mmlu_stem": { | |
| "acc,none": 0.6428797970187123, | |
| "acc_stderr,none": 0.008203846896313965, | |
| "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", | |
| "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": { | |
| "acc": true | |
| }, | |
| "mmlu_medical_genetics": { | |
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| "start_time": 14721447.548229909, | |
| "end_time": 14799728.273875136, | |
| "total_evaluation_time_seconds": "78280.72564522736" | |
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