# ruff: noqa: F405, F403, F401 """ Custom evaluation tasks for lighteval Do note that we ran the evals with `max_samples=1000` to speed up large evals. Most custom prompt changes were in an attempt to improve signal for small models in general. This file generally creates just a TASKS_TABLE and TASKS_GROUPS which are then imported by LightEval. Example usage (lighteval_tasks.py is the path to this file): =================== accelerate launch --num_processes=1 lighteval/run_evals_accelerate.py --model_args="pretrained=HuggingFaceFW/ablation-model-fineweb-edu" \ --custom_tasks "lighteval_tasks.py" --output_dir [OUTPUTPATH] --max_samples 1000 \ --tasks "custom|hellaswag|0|1,custom|winogrande|0|1,custom|piqa|0|1,custom|siqa|0|1,custom|openbookqa|0|1,custom|arc:easy|0|1,custom|arc:challenge|0|1,custom|commonsense_qa|0|1,custom|mmlu:abstract_algebra|0|1,custom|mmlu:anatomy|0|1,custom|mmlu:astronomy|0|1,custom|mmlu:business_ethics|0|1,custom|mmlu:clinical_knowledge|0|1,custom|mmlu:college_biology|0|1,custom|mmlu:college_chemistry|0|1,custom|mmlu:college_computer_science|0|1,custom|mmlu:college_mathematics|0|1,custom|mmlu:college_medicine|0|1,custom|mmlu:college_physics|0|1,custom|mmlu:computer_security|0|1,custom|mmlu:conceptual_physics|0|1,custom|mmlu:econometrics|0|1,custom|mmlu:electrical_engineering|0|1,custom|mmlu:elementary_mathematics|0|1,custom|mmlu:formal_logic|0|1,custom|mmlu:global_facts|0|1,custom|mmlu:high_school_biology|0|1,custom|mmlu:high_school_chemistry|0|1,custom|mmlu:high_school_computer_science|0|1,custom|mmlu:high_school_european_history|0|1,custom|mmlu:high_school_geography|0|1,custom|mmlu:high_school_government_and_politics|0|1,custom|mmlu:high_school_macroeconomics|0|1,custom|mmlu:high_school_mathematics|0|1,custom|mmlu:high_school_microeconomics|0|1,custom|mmlu:high_school_physics|0|1,custom|mmlu:high_school_psychology|0|1,custom|mmlu:high_school_statistics|0|1,custom|mmlu:high_school_us_history|0|1,custom|mmlu:high_school_world_history|0|1,custom|mmlu:human_aging|0|1,custom|mmlu:human_sexuality|0|1,custom|mmlu:international_law|0|1,custom|mmlu:jurisprudence|0|1,custom|mmlu:logical_fallacies|0|1,custom|mmlu:machine_learning|0|1,custom|mmlu:management|0|1,custom|mmlu:marketing|0|1,custom|mmlu:medical_genetics|0|1,custom|mmlu:miscellaneous|0|1,custom|mmlu:moral_disputes|0|1,custom|mmlu:moral_scenarios|0|1,custom|mmlu:nutrition|0|1,custom|mmlu:philosophy|0|1,custom|mmlu:prehistory|0|1,custom|mmlu:professional_accounting|0|1,custom|mmlu:professional_law|0|1,custom|mmlu:professional_medicine|0|1,custom|mmlu:professional_psychology|0|1,custom|mmlu:public_relations|0|1,custom|mmlu:security_studies|0|1,custom|mmlu:sociology|0|1,custom|mmlu:us_foreign_policy|0|1,custom|mmlu:virology|0|1,custom|mmlu:world_religions|0|1" =================== custom|cmmlu:agronomy|0|1,custom|cmmlu:anatomy|0|1,custom|cmmlu:ancient_chinese|0|1,custom|cmmlu:arts|0|1,custom|cmmlu:astronomy|0|1,custom|cmmlu:business_ethics|0|1,custom|cmmlu:chinese_civil_service_exam|0|1,custom|cmmlu:chinese_driving_rule|0|1,custom|cmmlu:chinese_food_culture|0|1,custom|cmmlu:chinese_foreign_policy|0|1,custom|cmmlu:chinese_history|0|1,custom|cmmlu:chinese_literature|0|1,custom|cmmlu:chinese_teacher_qualification|0|1,custom|cmmlu:clinical_knowledge|0|1,custom|cmmlu:college_actuarial_science|0|1,custom|cmmlu:college_education|0|1,custom|cmmlu:college_engineering_hydrology|0|1,custom|cmmlu:college_law|0|1,custom|cmmlu:college_mathematics|0|1,custom|cmmlu:college_medical_statistics|0|1,custom|cmmlu:college_medicine|0|1,custom|cmmlu:computer_science|0|1,custom|cmmlu:computer_security|0|1,custom|cmmlu:conceptual_physics|0|1,custom|cmmlu:construction_project_management|0|1,custom|cmmlu:economics|0|1,custom|cmmlu:education|0|1,custom|cmmlu:electrical_engineering|0|1,custom|cmmlu:elementary_chinese|0|1,custom|cmmlu:elementary_commonsense|0|1,custom|cmmlu:elementary_information_and_technology|0|1,custom|cmmlu:elementary_mathematics|0|1,custom|cmmlu:ethnology|0|1,custom|cmmlu:food_science|0|1,custom|cmmlu:genetics|0|1,custom|cmmlu:global_facts|0|1,custom|cmmlu:high_school_biology|0|1,custom|cmmlu:high_school_chemistry|0|1,custom|cmmlu:high_school_geography|0|1,custom|cmmlu:high_school_mathematics|0|1,custom|cmmlu:high_school_physics|0|1,custom|cmmlu:high_school_politics|0|1,custom|cmmlu:human_sexuality|0|1,custom|cmmlu:international_law|0|1,custom|cmmlu:journalism|0|1,custom|cmmlu:jurisprudence|0|1,custom|cmmlu:legal_and_moral_basis|0|1,custom|cmmlu:logical|0|1,custom|cmmlu:machine_learning|0|1,custom|cmmlu:management|0|1,custom|cmmlu:marketing|0|1,custom|cmmlu:marxist_theory|0|1,custom|cmmlu:modern_chinese|0|1,custom|cmmlu:nutrition|0|1,custom|cmmlu:philosophy|0|1,custom|cmmlu:professional_accounting|0|1,custom|cmmlu:professional_law|0|1,custom|cmmlu:professional_medicine|0|1,custom|cmmlu:professional_psychology|0|1,custom|cmmlu:public_relations|0|1,custom|cmmlu:security_study|0|1,custom|cmmlu:sociology|0|1,custom|cmmlu:sports_science|0|1,custom|cmmlu:traditional_chinese_medicine|0|1,custom|cmmlu:virology|0|1,custom|cmmlu:world_history|0|1,custom|cmmlu:world_religions|0|1 =================== custom|ceval:computer_network|0|1,custom|ceval:operating_system|0|1,custom|ceval:computer_architecture|0|1,custom|ceval:college_programming|0|1,custom|ceval:college_physics|0|1,custom|ceval:college_chemistry|0|1,custom|ceval:advanced_mathematics|0|1,custom|ceval:probability_and_statistics|0|1,custom|ceval:discrete_mathematics|0|1,custom|ceval:electrical_engineer|0|1,custom|ceval:metrology_engineer|0|1,custom|ceval:high_school_mathematics|0|1,custom|ceval:high_school_physics|0|1,custom|ceval:high_school_chemistry|0|1,custom|ceval:high_school_biology|0|1,custom|ceval:middle_school_mathematics|0|1,custom|ceval:middle_school_biology|0|1,custom|ceval:middle_school_physics|0|1,custom|ceval:middle_school_chemistry|0|1,custom|ceval:veterinary_medicine|0|1,custom|ceval:college_economics|0|1,custom|ceval:business_administration|0|1,custom|ceval:marxism|0|1,custom|ceval:mao_zedong_thought|0|1,custom|ceval:education_science|0|1,custom|ceval:teacher_qualification|0|1,custom|ceval:high_school_politics|0|1,custom|ceval:high_school_geography|0|1,custom|ceval:middle_school_politics|0|1,custom|ceval:middle_school_geography|0|1,custom|ceval:modern_chinese_history|0|1,custom|ceval:ideological_and_moral_cultivation|0|1,custom|ceval:logic|0|1,custom|ceval:law|0|1,custom|ceval:chinese_language_and_literature|0|1,custom|ceval:art_studies|0|1,custom|ceval:professional_tour_guide|0|1,custom|ceval:legal_professional|0|1,custom|ceval:high_school_chinese|0|1,custom|ceval:high_school_history|0|1,custom|ceval:middle_school_history|0|1,custom|ceval:civil_servant|0|1,custom|ceval:sports_science|0|1,custom|ceval:plant_protection|0|1,custom|ceval:basic_medicine|0|1,custom|ceval:clinical_medicine|0|1,custom|ceval:urban_and_rural_planner|0|1,custom|ceval:accountant|0|1,custom|ceval:fire_engineer|0|1,custom|ceval:environmental_impact_assessment_engineer|0|1,custom|ceval:tax_accountant|0|1,custom|ceval:physician|0|1 =================== More info here: https://github.com/huggingface/lighteval?tab=readme-ov-file#evaluate-a-model-on-extended-community-or-custom-tasks For more info on differences between MMLU implementations: https://huggingface.co/blog/open-llm-leaderboard-mmlu#1001-flavors-of-mmlu In particular, the default leaderboard MMLU implementation (which uses "A", "B", etc as answer targets) gives generally random results on small/non instruction tuned models. Instead, we use the full MMLU answer as the target. """ import re from typing import List, Tuple from lighteval.metrics import Metrics from lighteval.tasks.lighteval_task import LightevalTaskConfig from lighteval.tasks.requests import Doc from lighteval.tasks.tasks_prompt_formatting import LETTER_INDICES _TASKS_STRINGS: List[Tuple[LightevalTaskConfig, str]] = [] _TASKS: List[LightevalTaskConfig] = [] ## COMMON_SENSE_REASONING_TASKS ## COMMON_SENSE_REASONING_TASKS = [ LightevalTaskConfig( name="hellaswag", prompt_function="hellaswag_prompt", hf_repo="hellaswag", hf_subset="default", metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], ), LightevalTaskConfig( name="winogrande", prompt_function="winogrande", hf_repo="winogrande", hf_subset="winogrande_xl", metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], ), LightevalTaskConfig( name="piqa", prompt_function="piqa_harness", hf_repo="piqa", hf_subset="plain_text", metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], ), LightevalTaskConfig( name="siqa", prompt_function="siqa_prompt", hf_repo="lighteval/siqa", hf_subset="default", hf_avail_splits=["train", "validation"], metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], ), LightevalTaskConfig( name="openbookqa", prompt_function="openbookqa", hf_repo="openbookqa", hf_subset="main", metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], ), LightevalTaskConfig( name="arc:easy", prompt_function="arc", hf_repo="ai2_arc", hf_subset="ARC-Easy", evaluation_splits=["test"], generation_size=1, metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], ), LightevalTaskConfig( name="arc:challenge", prompt_function="arc", hf_repo="ai2_arc", hf_subset="ARC-Challenge", evaluation_splits=["test"], generation_size=1, metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], ), LightevalTaskConfig( name="commonsense_qa", prompt_function="commonsense_qa_prompt", hf_repo="commonsense_qa", hf_subset="default", metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], ), ] def commonsense_qa_prompt(line, task_name: str = None): return Doc( task_name=task_name, query=line["question"], choices=[f" {c}" for c in line["choices"]["text"]], gold_index=LETTER_INDICES.index(line["answerKey"].strip()), instruction="", ) def siqa_prompt(line, task_name: str = None): return Doc( task_name=task_name, query=line["context"] + " " + line["question"], choices=[f" {c}" for c in [line["answerA"], line["answerB"], line["answerC"]]], gold_index=int(line["label"]) - 1, instruction="", ) def hellaswag_prompt(line, task_name: str = None): def preprocess(text): """Comes from AiHarness""" # text = text.strip() # NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag. text = text.replace(" [title]", ". ") text = re.sub("\\[.*?\\]", "", text) text = text.replace(" ", " ") return text ctx = f"{line['ctx_a']} {line['ctx_b'].capitalize()} " return Doc( task_name=task_name, query=preprocess(line["activity_label"] + ": " + ctx), choices=[" " + preprocess(ending) for ending in line["endings"]], gold_index=int(line["label"]) if line["label"] != "" else -1, # -1 for test # "metric": "choices_loglikelihood", ) # 0 short for common sense COMMON_SENSE_REASONING_STRING = [(t, f"custom|{t.name}|0|1") for t in COMMON_SENSE_REASONING_TASKS] _TASKS_STRINGS.extend(COMMON_SENSE_REASONING_STRING) _TASKS += COMMON_SENSE_REASONING_TASKS ## MMLU ## class CustomMMLUEvaluationTask(LightevalTaskConfig): def __init__( self, name, prompt_function="mmlu_prompt", hf_repo="lighteval/mmlu", hf_subset=None, # metric=[Metrics.loglikelihood_acc_single_token], metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], hf_avail_splits=None, evaluation_splits=["test"], few_shots_split="dev", few_shots_select=None, suite=None, generation_size=-1, stop_sequence=None, output_regex=None, frozen=False, ): super().__init__( name=name, prompt_function=prompt_function, hf_repo=hf_repo, hf_subset=hf_subset, metric=metric, hf_avail_splits=hf_avail_splits, evaluation_splits=evaluation_splits, few_shots_split=few_shots_split, few_shots_select=few_shots_select, suite=suite, generation_size=generation_size, stop_sequence=stop_sequence, output_regex=output_regex, frozen=frozen, ) MMLU_TASKS = [ CustomMMLUEvaluationTask(name="mmlu:abstract_algebra", hf_subset="abstract_algebra"), CustomMMLUEvaluationTask(name="mmlu:anatomy", hf_subset="anatomy"), CustomMMLUEvaluationTask(name="mmlu:astronomy", hf_subset="astronomy"), CustomMMLUEvaluationTask(name="mmlu:business_ethics", hf_subset="business_ethics"), CustomMMLUEvaluationTask(name="mmlu:clinical_knowledge", hf_subset="clinical_knowledge"), CustomMMLUEvaluationTask(name="mmlu:college_biology", hf_subset="college_biology"), CustomMMLUEvaluationTask(name="mmlu:college_chemistry", hf_subset="college_chemistry"), CustomMMLUEvaluationTask(name="mmlu:college_computer_science", hf_subset="college_computer_science"), CustomMMLUEvaluationTask(name="mmlu:college_mathematics", hf_subset="college_mathematics"), CustomMMLUEvaluationTask(name="mmlu:college_medicine", hf_subset="college_medicine"), CustomMMLUEvaluationTask(name="mmlu:college_physics", hf_subset="college_physics"), CustomMMLUEvaluationTask(name="mmlu:computer_security", hf_subset="computer_security"), CustomMMLUEvaluationTask(name="mmlu:conceptual_physics", hf_subset="conceptual_physics"), CustomMMLUEvaluationTask(name="mmlu:econometrics", hf_subset="econometrics"), CustomMMLUEvaluationTask(name="mmlu:electrical_engineering", hf_subset="electrical_engineering"), CustomMMLUEvaluationTask(name="mmlu:elementary_mathematics", hf_subset="elementary_mathematics"), CustomMMLUEvaluationTask(name="mmlu:formal_logic", hf_subset="formal_logic"), CustomMMLUEvaluationTask(name="mmlu:global_facts", hf_subset="global_facts"), CustomMMLUEvaluationTask(name="mmlu:high_school_biology", hf_subset="high_school_biology"), CustomMMLUEvaluationTask(name="mmlu:high_school_chemistry", hf_subset="high_school_chemistry"), CustomMMLUEvaluationTask(name="mmlu:high_school_computer_science", hf_subset="high_school_computer_science"), CustomMMLUEvaluationTask(name="mmlu:high_school_european_history", hf_subset="high_school_european_history"), CustomMMLUEvaluationTask(name="mmlu:high_school_geography", hf_subset="high_school_geography"), CustomMMLUEvaluationTask( name="mmlu:high_school_government_and_politics", hf_subset="high_school_government_and_politics" ), CustomMMLUEvaluationTask(name="mmlu:high_school_macroeconomics", hf_subset="high_school_macroeconomics"), CustomMMLUEvaluationTask(name="mmlu:high_school_mathematics", hf_subset="high_school_mathematics"), CustomMMLUEvaluationTask(name="mmlu:high_school_microeconomics", hf_subset="high_school_microeconomics"), CustomMMLUEvaluationTask(name="mmlu:high_school_physics", hf_subset="high_school_physics"), CustomMMLUEvaluationTask(name="mmlu:high_school_psychology", hf_subset="high_school_psychology"), CustomMMLUEvaluationTask(name="mmlu:high_school_statistics", hf_subset="high_school_statistics"), CustomMMLUEvaluationTask(name="mmlu:high_school_us_history", hf_subset="high_school_us_history"), CustomMMLUEvaluationTask(name="mmlu:high_school_world_history", hf_subset="high_school_world_history"), CustomMMLUEvaluationTask(name="mmlu:human_aging", hf_subset="human_aging"), CustomMMLUEvaluationTask(name="mmlu:human_sexuality", hf_subset="human_sexuality"), CustomMMLUEvaluationTask(name="mmlu:international_law", hf_subset="international_law"), CustomMMLUEvaluationTask(name="mmlu:jurisprudence", hf_subset="jurisprudence"), CustomMMLUEvaluationTask(name="mmlu:logical_fallacies", hf_subset="logical_fallacies"), CustomMMLUEvaluationTask(name="mmlu:machine_learning", hf_subset="machine_learning"), CustomMMLUEvaluationTask(name="mmlu:management", hf_subset="management"), CustomMMLUEvaluationTask(name="mmlu:marketing", hf_subset="marketing"), CustomMMLUEvaluationTask(name="mmlu:medical_genetics", hf_subset="medical_genetics"), CustomMMLUEvaluationTask(name="mmlu:miscellaneous", hf_subset="miscellaneous"), CustomMMLUEvaluationTask(name="mmlu:moral_disputes", hf_subset="moral_disputes"), CustomMMLUEvaluationTask(name="mmlu:moral_scenarios", hf_subset="moral_scenarios"), CustomMMLUEvaluationTask(name="mmlu:nutrition", hf_subset="nutrition"), CustomMMLUEvaluationTask(name="mmlu:philosophy", hf_subset="philosophy"), CustomMMLUEvaluationTask(name="mmlu:prehistory", hf_subset="prehistory"), CustomMMLUEvaluationTask(name="mmlu:professional_accounting", hf_subset="professional_accounting"), CustomMMLUEvaluationTask(name="mmlu:professional_law", hf_subset="professional_law"), CustomMMLUEvaluationTask(name="mmlu:professional_medicine", hf_subset="professional_medicine"), CustomMMLUEvaluationTask(name="mmlu:professional_psychology", hf_subset="professional_psychology"), CustomMMLUEvaluationTask(name="mmlu:public_relations", hf_subset="public_relations"), CustomMMLUEvaluationTask(name="mmlu:security_studies", hf_subset="security_studies"), CustomMMLUEvaluationTask(name="mmlu:sociology", hf_subset="sociology"), CustomMMLUEvaluationTask(name="mmlu:us_foreign_policy", hf_subset="us_foreign_policy"), CustomMMLUEvaluationTask(name="mmlu:virology", hf_subset="virology"), CustomMMLUEvaluationTask(name="mmlu:world_religions", hf_subset="world_religions"), ] def mmlu_prompt(line, task_name: str = None): """MMLU prompt without letters""" topic = line["subject"] prompt = f"The following are questions about {topic.replace('_', ' ')}.\nQuestion: " prompt += line["question"] + "\nAnswer:" #print(f"mmlu_prompt={prompt}") return Doc( task_name=task_name, query=prompt, choices=[f" {c}" for c in line["choices"]], gold_index=line["answer"], instruction=f"The following are questions about {topic.replace('_', ' ')}.\n", ) MMLU_STRING = [(t, f"custom|{t.name}|0|1") for t in MMLU_TASKS] _TASKS_STRINGS.extend(MMLU_STRING) _TASKS += MMLU_TASKS ############################################################################################################################################################ ## CMMLU ## class CustomCMMLUEvaluationTask(LightevalTaskConfig): def __init__( self, name, prompt_function="cmmlu_prompt", hf_repo="ldwang/lighteval-cmmlu", hf_subset=None, # metric=[Metrics.loglikelihood_acc_single_token], metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], hf_avail_splits=None, evaluation_splits=["test"], few_shots_split="dev", few_shots_select=None, suite=None, generation_size=-1, stop_sequence=None, output_regex=None, frozen=False, ): super().__init__( name=name, prompt_function=prompt_function, hf_repo=hf_repo, hf_subset=hf_subset, metric=metric, hf_avail_splits=hf_avail_splits, evaluation_splits=evaluation_splits, few_shots_split=few_shots_split, few_shots_select=few_shots_select, suite=suite, generation_size=generation_size, stop_sequence=stop_sequence, output_regex=output_regex, frozen=frozen, trust_dataset=True, ) CMMLU_TASKS = [ CustomCMMLUEvaluationTask(name="cmmlu:agronomy", hf_subset="agronomy"), CustomCMMLUEvaluationTask(name="cmmlu:anatomy", hf_subset="anatomy"), CustomCMMLUEvaluationTask(name="cmmlu:ancient_chinese", hf_subset="ancient_chinese"), CustomCMMLUEvaluationTask(name="cmmlu:arts", hf_subset="arts"), CustomCMMLUEvaluationTask(name="cmmlu:astronomy", hf_subset="astronomy"), CustomCMMLUEvaluationTask(name="cmmlu:business_ethics", hf_subset="business_ethics"), CustomCMMLUEvaluationTask(name="cmmlu:chinese_civil_service_exam", hf_subset="chinese_civil_service_exam"), CustomCMMLUEvaluationTask(name="cmmlu:chinese_driving_rule", hf_subset="chinese_driving_rule"), CustomCMMLUEvaluationTask(name="cmmlu:chinese_food_culture", hf_subset="chinese_food_culture"), CustomCMMLUEvaluationTask(name="cmmlu:chinese_foreign_policy", hf_subset="chinese_foreign_policy"), CustomCMMLUEvaluationTask(name="cmmlu:chinese_history", hf_subset="chinese_history"), CustomCMMLUEvaluationTask(name="cmmlu:chinese_literature", hf_subset="chinese_literature"), CustomCMMLUEvaluationTask(name="cmmlu:chinese_teacher_qualification", hf_subset="chinese_teacher_qualification"), CustomCMMLUEvaluationTask(name="cmmlu:clinical_knowledge", hf_subset="clinical_knowledge"), CustomCMMLUEvaluationTask(name="cmmlu:college_actuarial_science", hf_subset="college_actuarial_science"), CustomCMMLUEvaluationTask(name="cmmlu:college_education", hf_subset="college_education"), CustomCMMLUEvaluationTask(name="cmmlu:college_engineering_hydrology", hf_subset="college_engineering_hydrology"), CustomCMMLUEvaluationTask(name="cmmlu:college_law", hf_subset="college_law"), CustomCMMLUEvaluationTask(name="cmmlu:college_mathematics", hf_subset="college_mathematics"), CustomCMMLUEvaluationTask(name="cmmlu:college_medical_statistics", hf_subset="college_medical_statistics"), CustomCMMLUEvaluationTask(name="cmmlu:college_medicine", hf_subset="college_medicine"), CustomCMMLUEvaluationTask(name="cmmlu:computer_science", hf_subset="computer_science"), CustomCMMLUEvaluationTask(name="cmmlu:computer_security", hf_subset="computer_security"), CustomCMMLUEvaluationTask(name="cmmlu:conceptual_physics", hf_subset="conceptual_physics"), CustomCMMLUEvaluationTask(name="cmmlu:construction_project_management", hf_subset="construction_project_management"), CustomCMMLUEvaluationTask(name="cmmlu:economics", hf_subset="economics"), CustomCMMLUEvaluationTask(name="cmmlu:education", hf_subset="education"), CustomCMMLUEvaluationTask(name="cmmlu:electrical_engineering", hf_subset="electrical_engineering"), CustomCMMLUEvaluationTask(name="cmmlu:elementary_chinese", hf_subset="elementary_chinese"), CustomCMMLUEvaluationTask(name="cmmlu:elementary_commonsense", hf_subset="elementary_commonsense"), CustomCMMLUEvaluationTask(name="cmmlu:elementary_information_and_technology", hf_subset="elementary_information_and_technology"), CustomCMMLUEvaluationTask(name="cmmlu:elementary_mathematics", hf_subset="elementary_mathematics"), CustomCMMLUEvaluationTask(name="cmmlu:ethnology", hf_subset="ethnology"), CustomCMMLUEvaluationTask(name="cmmlu:food_science", hf_subset="food_science"), CustomCMMLUEvaluationTask(name="cmmlu:genetics", hf_subset="genetics"), CustomCMMLUEvaluationTask(name="cmmlu:global_facts", hf_subset="global_facts"), CustomCMMLUEvaluationTask(name="cmmlu:high_school_biology", hf_subset="high_school_biology"), CustomCMMLUEvaluationTask(name="cmmlu:high_school_chemistry", hf_subset="high_school_chemistry"), CustomCMMLUEvaluationTask(name="cmmlu:high_school_geography", hf_subset="high_school_geography"), CustomCMMLUEvaluationTask(name="cmmlu:high_school_mathematics", hf_subset="high_school_mathematics"), CustomCMMLUEvaluationTask(name="cmmlu:high_school_physics", hf_subset="high_school_physics"), CustomCMMLUEvaluationTask(name="cmmlu:high_school_politics", hf_subset="high_school_politics"), CustomCMMLUEvaluationTask(name="cmmlu:human_sexuality", hf_subset="human_sexuality"), CustomCMMLUEvaluationTask(name="cmmlu:international_law", hf_subset="international_law"), CustomCMMLUEvaluationTask(name="cmmlu:journalism", hf_subset="journalism"), CustomCMMLUEvaluationTask(name="cmmlu:jurisprudence", hf_subset="jurisprudence"), CustomCMMLUEvaluationTask(name="cmmlu:legal_and_moral_basis", hf_subset="legal_and_moral_basis"), CustomCMMLUEvaluationTask(name="cmmlu:logical", hf_subset="logical"), CustomCMMLUEvaluationTask(name="cmmlu:machine_learning", hf_subset="machine_learning"), CustomCMMLUEvaluationTask(name="cmmlu:management", hf_subset="management"), CustomCMMLUEvaluationTask(name="cmmlu:marketing", hf_subset="marketing"), CustomCMMLUEvaluationTask(name="cmmlu:marxist_theory", hf_subset="marxist_theory"), CustomCMMLUEvaluationTask(name="cmmlu:modern_chinese", hf_subset="modern_chinese"), CustomCMMLUEvaluationTask(name="cmmlu:nutrition", hf_subset="nutrition"), CustomCMMLUEvaluationTask(name="cmmlu:philosophy", hf_subset="philosophy"), CustomCMMLUEvaluationTask(name="cmmlu:professional_accounting", hf_subset="professional_accounting"), CustomCMMLUEvaluationTask(name="cmmlu:professional_law", hf_subset="professional_law"), CustomCMMLUEvaluationTask(name="cmmlu:professional_medicine", hf_subset="professional_medicine"), CustomCMMLUEvaluationTask(name="cmmlu:professional_psychology", hf_subset="professional_psychology"), CustomCMMLUEvaluationTask(name="cmmlu:public_relations", hf_subset="public_relations"), CustomCMMLUEvaluationTask(name="cmmlu:security_study", hf_subset="security_study"), CustomCMMLUEvaluationTask(name="cmmlu:sociology", hf_subset="sociology"), CustomCMMLUEvaluationTask(name="cmmlu:sports_science", hf_subset="sports_science"), CustomCMMLUEvaluationTask(name="cmmlu:traditional_chinese_medicine", hf_subset="traditional_chinese_medicine"), CustomCMMLUEvaluationTask(name="cmmlu:virology", hf_subset="virology"), CustomCMMLUEvaluationTask(name="cmmlu:world_history", hf_subset="world_history"), CustomCMMLUEvaluationTask(name="cmmlu:world_religions", hf_subset="world_religions"), ] cmmlu_subject_mapping = { 'agronomy': '农学', 'anatomy': '解剖学', 'ancient_chinese': '古汉语', 'arts': '艺术学', 'astronomy': '天文学', 'business_ethics': '商业伦理', 'chinese_civil_service_exam': '中国公务员考试', 'chinese_driving_rule': '中国驾驶规则', 'chinese_food_culture': '中国饮食文化', 'chinese_foreign_policy': '中国外交政策', 'chinese_history': '中国历史', 'chinese_literature': '中国文学', 'chinese_teacher_qualification': '中国教师资格', 'clinical_knowledge': '临床知识', 'college_actuarial_science': '大学精算学', 'college_education': '大学教育学', 'college_engineering_hydrology': '大学工程水文学', 'college_law': '大学法律', 'college_mathematics': '大学数学', 'college_medical_statistics': '大学医学统计', 'college_medicine': '大学医学', 'computer_science': '计算机科学', 'computer_security': '计算机安全', 'conceptual_physics': '概念物理学', 'construction_project_management': '建设工程管理', 'economics': '经济学', 'education': '教育学', 'electrical_engineering': '电气工程', 'elementary_chinese': '小学语文', 'elementary_commonsense': '小学常识', 'elementary_information_and_technology': '小学信息技术', 'elementary_mathematics': '初等数学', 'ethnology': '民族学', 'food_science': '食品科学', 'genetics': '遗传学', 'global_facts': '全球事实', 'high_school_biology': '高中生物', 'high_school_chemistry': '高中化学', 'high_school_geography': '高中地理', 'high_school_mathematics': '高中数学', 'high_school_physics': '高中物理学', 'high_school_politics': '高中政治', 'human_sexuality': '人类性行为', 'international_law': '国际法学', 'journalism': '新闻学', 'jurisprudence': '法理学', 'legal_and_moral_basis': '法律与道德基础', 'logical': '逻辑学', 'machine_learning': '机器学习', 'management': '管理学', 'marketing': '市场营销', 'marxist_theory': '马克思主义理论', 'modern_chinese': '现代汉语', 'nutrition': '营养学', 'philosophy': '哲学', 'professional_accounting': '专业会计', 'professional_law': '专业法学', 'professional_medicine': '专业医学', 'professional_psychology': '专业心理学', 'public_relations': '公共关系', 'security_study': '安全研究', 'sociology': '社会学', 'sports_science': '体育学', 'traditional_chinese_medicine': '中医中药', 'virology': '病毒学', 'world_history': '世界历史', 'world_religions': '世界宗教' } def cmmlu_prompt(line, task_name: str = None): # 以下是关于{_ch_name}的单项选择题，请直接给出正确答案的选项。\n题目：{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}} # 答案是: {{{answer}}} """CMMLU prompt without letters""" topic = cmmlu_subject_mapping[line['subject']] prompt = f"以下是关于{topic.replace('_', ' ')}的单项选择题，请直接给出正确答案的选项。\n题目：" prompt += line["question"] + "\n答案是：" #print(f"cmmlu_prompt={prompt}") return Doc( task_name=task_name, query=prompt, choices=[f" {c}" for c in line["choices"]], gold_index=line["answer"], instruction=None, ) CMMLU_STRING = [(t, f"custom|{t.name}|0|1") for t in CMMLU_TASKS] _TASKS_STRINGS.extend(CMMLU_STRING) _TASKS += CMMLU_TASKS print(f'{",".join([t[1] for t in CMMLU_STRING])}') ############################################################################################################################################################ ## CEVAL ## class CustomCEVALEvaluationTask(LightevalTaskConfig): def __init__( self, name, prompt_function="ceval_prompt", hf_repo="ldwang/lighteval-ceval-exam", hf_subset=None, # metric=[Metrics.loglikelihood_acc_single_token], metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], hf_avail_splits=None, evaluation_splits=["val"], few_shots_split="dev", few_shots_select=None, suite=None, generation_size=-1, stop_sequence=None, output_regex=None, frozen=False, ): super().__init__( name=name, prompt_function=prompt_function, hf_repo=hf_repo, hf_subset=hf_subset, metric=metric, hf_avail_splits=hf_avail_splits, evaluation_splits=evaluation_splits, few_shots_split=few_shots_split, few_shots_select=few_shots_select, suite=suite, generation_size=generation_size, stop_sequence=stop_sequence, output_regex=output_regex, frozen=frozen, trust_dataset=True, ) CEVAL_TASKS = [ CustomCEVALEvaluationTask(name="ceval:computer_network", hf_subset="computer_network"), CustomCEVALEvaluationTask(name="ceval:operating_system", hf_subset="operating_system"), CustomCEVALEvaluationTask(name="ceval:computer_architecture", hf_subset="computer_architecture"), CustomCEVALEvaluationTask(name="ceval:college_programming", hf_subset="college_programming"), CustomCEVALEvaluationTask(name="ceval:college_physics", hf_subset="college_physics"), CustomCEVALEvaluationTask(name="ceval:college_chemistry", hf_subset="college_chemistry"), CustomCEVALEvaluationTask(name="ceval:advanced_mathematics", hf_subset="advanced_mathematics"), CustomCEVALEvaluationTask(name="ceval:probability_and_statistics", hf_subset="probability_and_statistics"), CustomCEVALEvaluationTask(name="ceval:discrete_mathematics", hf_subset="discrete_mathematics"), CustomCEVALEvaluationTask(name="ceval:electrical_engineer", hf_subset="electrical_engineer"), CustomCEVALEvaluationTask(name="ceval:metrology_engineer", hf_subset="metrology_engineer"), CustomCEVALEvaluationTask(name="ceval:high_school_mathematics", hf_subset="high_school_mathematics"), CustomCEVALEvaluationTask(name="ceval:high_school_physics", hf_subset="high_school_physics"), CustomCEVALEvaluationTask(name="ceval:high_school_chemistry", hf_subset="high_school_chemistry"), CustomCEVALEvaluationTask(name="ceval:high_school_biology", hf_subset="high_school_biology"), CustomCEVALEvaluationTask(name="ceval:middle_school_mathematics", hf_subset="middle_school_mathematics"), CustomCEVALEvaluationTask(name="ceval:middle_school_biology", hf_subset="middle_school_biology"), CustomCEVALEvaluationTask(name="ceval:middle_school_physics", hf_subset="middle_school_physics"), CustomCEVALEvaluationTask(name="ceval:middle_school_chemistry", hf_subset="middle_school_chemistry"), CustomCEVALEvaluationTask(name="ceval:veterinary_medicine", hf_subset="veterinary_medicine"), CustomCEVALEvaluationTask(name="ceval:college_economics", hf_subset="college_economics"), CustomCEVALEvaluationTask(name="ceval:business_administration", hf_subset="business_administration"), CustomCEVALEvaluationTask(name="ceval:marxism", hf_subset="marxism"), CustomCEVALEvaluationTask(name="ceval:mao_zedong_thought", hf_subset="mao_zedong_thought"), CustomCEVALEvaluationTask(name="ceval:education_science", hf_subset="education_science"), CustomCEVALEvaluationTask(name="ceval:teacher_qualification", hf_subset="teacher_qualification"), CustomCEVALEvaluationTask(name="ceval:high_school_politics", hf_subset="high_school_politics"), CustomCEVALEvaluationTask(name="ceval:high_school_geography", hf_subset="high_school_geography"), CustomCEVALEvaluationTask(name="ceval:middle_school_politics", hf_subset="middle_school_politics"), CustomCEVALEvaluationTask(name="ceval:middle_school_geography", hf_subset="middle_school_geography"), CustomCEVALEvaluationTask(name="ceval:modern_chinese_history", hf_subset="modern_chinese_history"), CustomCEVALEvaluationTask(name="ceval:ideological_and_moral_cultivation", hf_subset="ideological_and_moral_cultivation"), CustomCEVALEvaluationTask(name="ceval:logic", hf_subset="logic"), CustomCEVALEvaluationTask(name="ceval:law", hf_subset="law"), CustomCEVALEvaluationTask(name="ceval:chinese_language_and_literature", hf_subset="chinese_language_and_literature"), CustomCEVALEvaluationTask(name="ceval:art_studies", hf_subset="art_studies"), CustomCEVALEvaluationTask(name="ceval:professional_tour_guide", hf_subset="professional_tour_guide"), CustomCEVALEvaluationTask(name="ceval:legal_professional", hf_subset="legal_professional"), CustomCEVALEvaluationTask(name="ceval:high_school_chinese", hf_subset="high_school_chinese"), CustomCEVALEvaluationTask(name="ceval:high_school_history", hf_subset="high_school_history"), CustomCEVALEvaluationTask(name="ceval:middle_school_history", hf_subset="middle_school_history"), CustomCEVALEvaluationTask(name="ceval:civil_servant", hf_subset="civil_servant"), CustomCEVALEvaluationTask(name="ceval:sports_science", hf_subset="sports_science"), CustomCEVALEvaluationTask(name="ceval:plant_protection", hf_subset="plant_protection"), CustomCEVALEvaluationTask(name="ceval:basic_medicine", hf_subset="basic_medicine"), CustomCEVALEvaluationTask(name="ceval:clinical_medicine", hf_subset="clinical_medicine"), CustomCEVALEvaluationTask(name="ceval:urban_and_rural_planner", hf_subset="urban_and_rural_planner"), CustomCEVALEvaluationTask(name="ceval:accountant", hf_subset="accountant"), CustomCEVALEvaluationTask(name="ceval:fire_engineer", hf_subset="fire_engineer"), CustomCEVALEvaluationTask(name="ceval:environmental_impact_assessment_engineer", hf_subset="environmental_impact_assessment_engineer"), CustomCEVALEvaluationTask(name="ceval:tax_accountant", hf_subset="tax_accountant"), CustomCEVALEvaluationTask(name="ceval:physician", hf_subset="physician"), ] ceval_subject_mapping = { 'computer_network': ['Computer Network', '计算机网络', 'STEM'], 'operating_system': ['Operating System', '操作系统', 'STEM'], 'computer_architecture': ['Computer Architecture', '计算机组成', 'STEM'], 'college_programming': ['College Programming', '大学编程', 'STEM'], 'college_physics': ['College Physics', '大学物理', 'STEM'], 'college_chemistry': ['College Chemistry', '大学化学', 'STEM'], 'advanced_mathematics': ['Advanced Mathematics', '高等数学', 'STEM'], 'probability_and_statistics': ['Probability and Statistics', '概率统计', 'STEM'], 'discrete_mathematics': ['Discrete Mathematics', '离散数学', 'STEM'], 'electrical_engineer': ['Electrical Engineer', '注册电气工程师', 'STEM'], 'metrology_engineer': ['Metrology Engineer', '注册计量师', 'STEM'], 'high_school_mathematics': ['High School Mathematics', '高中数学', 'STEM'], 'high_school_physics': ['High School Physics', '高中物理', 'STEM'], 'high_school_chemistry': ['High School Chemistry', '高中化学', 'STEM'], 'high_school_biology': ['High School Biology', '高中生物', 'STEM'], 'middle_school_mathematics': ['Middle School Mathematics', '初中数学', 'STEM'], 'middle_school_biology': ['Middle School Biology', '初中生物', 'STEM'], 'middle_school_physics': ['Middle School Physics', '初中物理', 'STEM'], 'middle_school_chemistry': ['Middle School Chemistry', '初中化学', 'STEM'], 'veterinary_medicine': ['Veterinary Medicine', '兽医学', 'STEM'], 'college_economics': ['College Economics', '大学经济学', 'Social Science'], 'business_administration': ['Business Administration', '工商管理', 'Social Science'], 'marxism': ['Marxism', '马克思主义基本原理', 'Social Science'], 'mao_zedong_thought': ['Mao Zedong Thought', '毛泽东思想和中国特色社会主义理论体系概论', 'Social Science'], 'education_science': ['Education Science', '教育学', 'Social Science'], 'teacher_qualification': ['Teacher Qualification', '教师资格', 'Social Science'], 'high_school_politics': ['High School Politics', '高中政治', 'Social Science'], 'high_school_geography': ['High School Geography', '高中地理', 'Social Science'], 'middle_school_politics': ['Middle School Politics', '初中政治', 'Social Science'], 'middle_school_geography': ['Middle School Geography', '初中地理', 'Social Science'], 'modern_chinese_history': ['Modern Chinese History', '近代史纲要', 'Humanities'], 'ideological_and_moral_cultivation': ['Ideological and Moral Cultivation', '思想道德修养与法律基础', 'Humanities'], 'logic': ['Logic', '逻辑学', 'Humanities'], 'law': ['Law', '法学', 'Humanities'], 'chinese_language_and_literature': ['Chinese Language and Literature', '中国语言文学', 'Humanities'], 'art_studies': ['Art Studies', '艺术学', 'Humanities'], 'professional_tour_guide': ['Professional Tour Guide', '导游资格', 'Humanities'], 'legal_professional': ['Legal Professional', '法律职业资格', 'Humanities'], 'high_school_chinese': ['High School Chinese', '高中语文', 'Humanities'], 'high_school_history': ['High School History', '高中历史', 'Humanities'], 'middle_school_history': ['Middle School History', '初中历史', 'Humanities'], 'civil_servant': ['Civil Servant', '公务员', 'Other'], 'sports_science': ['Sports Science', '体育学', 'Other'], 'plant_protection': ['Plant Protection', '植物保护', 'Other'], 'basic_medicine': ['Basic Medicine', '基础医学', 'Other'], 'clinical_medicine': ['Clinical Medicine', '临床医学', 'Other'], 'urban_and_rural_planner': ['Urban and Rural Planner', '注册城乡规划师', 'Other'], 'accountant': ['Accountant', '注册会计师', 'Other'], 'fire_engineer': ['Fire Engineer', '注册消防工程师', 'Other'], 'environmental_impact_assessment_engineer': ['Environmental Impact Assessment Engineer', '环境影响评价工程师', 'Other'], 'tax_accountant': ['Tax Accountant', '税务师', 'Other'], 'physician': ['Physician', '医师资格', 'Other'], } def ceval_prompt(line, task_name: str = None): # f"以下是中国关于{_ch_name}考试的单项选择题，请选出其中的正确答案。\n{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案: " """CEVAL prompt without letters""" topic = ceval_subject_mapping[line['subject']][1] prompt = f"以下是中国关于{topic.replace('_', ' ')}考试的单项选择题，请选出其中的正确答案。\n题目：" prompt += line["question"] + "\n答案：" #print(f"ceval_prompt={prompt}") return Doc( task_name=task_name, query=prompt, choices=[f" {c}" for c in line["choices"]], gold_index=line["answer"], instruction=None, ) CEVAL_STRING = [(t, f"custom|{t.name}|0|1") for t in CEVAL_TASKS] _TASKS_STRINGS.extend(CEVAL_STRING) _TASKS += CEVAL_TASKS print(f'{",".join([t[1] for t in CEVAL_STRING])}') ############################################################################################################################################################ # common sense reasoning + mmlu EARLY_SIGNAL_TASKS = ",".join([t[1] for t in COMMON_SENSE_REASONING_STRING] + [t[1] for t in MMLU_STRING] + [t[1] for t in CMMLU_STRING]) # Convert to dict for lighteval TASKS_TABLE = [task.as_dict() for task in _TASKS] # You can have a few pre-organised groups of tasks TASKS_GROUPS = { "early-signal": EARLY_SIGNAL_TASKS, }