add Ultra-FineWeb lighteval task python file

evaluation/lighteval_tasks_ultrafineweb.py

@@ -0,0 +1,645 @@
+# 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"
+===================
+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="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="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="validation:",
+        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:"
+
+    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
+
+# common sense reasoning + mmlu
+EARLY_SIGNAL_TASKS = ",".join([t[1] for t in COMMON_SENSE_REASONING_STRING] + [t[1] for t in MMLU_STRING])
+
+## CMMLU ##
+class CustomCMMLUEvaluationTask(LightevalTaskConfig):
+    def __init__(
+        self,
+        name,
+        prompt_function="cmmlu_prompt",
+        hf_repo="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="validation",
+        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,
+        )
+
+
+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):
+    """CMMLU prompt without letters"""
+    topic = line["subject"]
+    _ch_name = CMMLU_SUBJECT_MAPPING[topic]
+    prompt = f"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："
+    prompt += line["question"] + "\n答案是:"
+
+    return Doc(
+        task_name=task_name,
+        query=prompt,
+        choices=[f" {c}" for c in line["choices"]],
+        gold_index=line["answer"],
+        instruction=f"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",
+    )
+
+
+CMMLU_STRING = [(t, f"custom|{t.name}|0|1") for t in CMMLU_TASKS]
+_TASKS_STRINGS.extend(CMMLU_STRING)
+_TASKS += CMMLU_TASKS
+
+# cmmlu
+EARLY_SIGNAL_TASKS = ",".join([t[1] for t in COMMON_SENSE_REASONING_STRING] + [t[1] for t in CMMLU_STRING])
+
+
+## CEVAL ##
+class CustomCEVALEvaluationTask(LightevalTaskConfig):
+    def __init__(
+        self,
+        name,
+        prompt_function="ceval_prompt",
+        hf_repo="ceval",
+        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="validation",
+        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,
+        )
+
+
+CEVAL_TASKS = [
+    CustomCEVALEvaluationTask(name="ceval:accountant",hf_subset="accountant"),
+    CustomCEVALEvaluationTask(name="ceval:advanced_mathematics", hf_subset="advanced_mathematics"),
+    CustomCEVALEvaluationTask(name="ceval:art_studies", hf_subset="art_studies"),
+    CustomCEVALEvaluationTask(name="ceval:basic_medicine", hf_subset="basic_medicine"),
+    CustomCEVALEvaluationTask(name="ceval:business_administration", hf_subset="business_administration"),
+    CustomCEVALEvaluationTask(name="ceval:chinese_language_and_literature", hf_subset="chinese_language_and_literature"),
+    CustomCEVALEvaluationTask(name="ceval:civil_servant", hf_subset="civil_servant"),
+    CustomCEVALEvaluationTask(name="ceval:clinical_medicine", hf_subset="clinical_medicine"),
+    CustomCEVALEvaluationTask(name="ceval:college_chemistry", hf_subset="college_chemistry"),
+    CustomCEVALEvaluationTask(name="ceval:college_economics", hf_subset="college_economics"),
+    CustomCEVALEvaluationTask(name="ceval:college_physics", hf_subset="college_physics"),
+    CustomCEVALEvaluationTask(name="ceval:college_programming", hf_subset="college_programming"),
+    CustomCEVALEvaluationTask(name="ceval:computer_architecture", hf_subset="computer_architecture"),
+    CustomCEVALEvaluationTask(name="ceval:computer_network", hf_subset="computer_network"),
+    CustomCEVALEvaluationTask(name="ceval:discrete_mathematics", hf_subset="discrete_mathematics"),
+    CustomCEVALEvaluationTask(name="ceval:education_science", hf_subset="education_science"),
+    CustomCEVALEvaluationTask(name="ceval:electrical_engineer", hf_subset="electrical_engineer"),
+    CustomCEVALEvaluationTask(name="ceval:environmental_impact_assessment_engineer", hf_subset="environmental_impact_assessment_engineer"),
+    CustomCEVALEvaluationTask(name="ceval:fire_engineer", hf_subset="fire_engineer"),
+    CustomCEVALEvaluationTask(name="ceval:high_school_biology", hf_subset="high_school_biology"),
+    CustomCEVALEvaluationTask(name="ceval:high_school_chemistry", hf_subset="high_school_chemistry"),
+    CustomCEVALEvaluationTask(name="ceval:high_school_chinese", hf_subset="high_school_chinese"),
+    CustomCEVALEvaluationTask(name="ceval:high_school_geography", hf_subset="high_school_geography"),
+    CustomCEVALEvaluationTask(name="ceval:high_school_history", hf_subset="high_school_history"),
+    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_politics", hf_subset="high_school_politics"),
+    CustomCEVALEvaluationTask(name="ceval:ideological_and_moral_cultivation", hf_subset="ideological_and_moral_cultivation"),
+    CustomCEVALEvaluationTask(name="ceval:law", hf_subset="law"),
+    CustomCEVALEvaluationTask(name="ceval:legal_professional", hf_subset="legal_professional"),
+    CustomCEVALEvaluationTask(name="ceval:logic", hf_subset="logic"),
+    CustomCEVALEvaluationTask(name="ceval:mao_zedong_thought", hf_subset="mao_zedong_thought"),
+    CustomCEVALEvaluationTask(name="ceval:marxism", hf_subset="marxism"),
+    CustomCEVALEvaluationTask(name="ceval:metrology_engineer", hf_subset="metrology_engineer"),
+    CustomCEVALEvaluationTask(name="ceval:middle_school_biology", hf_subset="middle_school_biology"),
+    CustomCEVALEvaluationTask(name="ceval:middle_school_chemistry", hf_subset="middle_school_chemistry"),
+    CustomCEVALEvaluationTask(name="ceval:middle_school_geography", hf_subset="middle_school_geography"),
+    CustomCEVALEvaluationTask(name="ceval:middle_school_history", hf_subset="middle_school_history"),
+    CustomCEVALEvaluationTask(name="ceval:middle_school_mathematics", hf_subset="middle_school_mathematics"),
+    CustomCEVALEvaluationTask(name="ceval:middle_school_physics", hf_subset="middle_school_physics"),
+    CustomCEVALEvaluationTask(name="ceval:middle_school_politics", hf_subset="middle_school_politics"),
+    CustomCEVALEvaluationTask(name="ceval:modern_chinese_history", hf_subset="modern_chinese_history"),
+    CustomCEVALEvaluationTask(name="ceval:operating_system", hf_subset="operating_system"),
+    CustomCEVALEvaluationTask(name="ceval:physician", hf_subset="physician"),
+    CustomCEVALEvaluationTask(name="ceval:plant_protection", hf_subset="plant_protection"),
+    CustomCEVALEvaluationTask(name="ceval:probability_and_statistics", hf_subset="probability_and_statistics"),
+    CustomCEVALEvaluationTask(name="ceval:professional_tour_guide", hf_subset="professional_tour_guide"),
+    CustomCEVALEvaluationTask(name="ceval:sports_science", hf_subset="sports_science"),
+    CustomCEVALEvaluationTask(name="ceval:tax_accountant", hf_subset="tax_accountant"),
+    CustomCEVALEvaluationTask(name="ceval:teacher_qualification", hf_subset="teacher_qualification"),
+    CustomCEVALEvaluationTask(name="ceval:urban_and_rural_planner", hf_subset="urban_and_rural_planner"),
+    CustomCEVALEvaluationTask(name="ceval:veterinary_medicine", hf_subset="veterinary_medicine")
+]
+
+
+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):
+    """CEVAL prompt without letters"""
+    topic = line["subject"]
+    _ch_name = CEVAL_SUBJECT_MAPPING[topic][1]
+    prompt = f"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："
+    prompt += line["question"] + "\n答案是:"
+
+    return Doc(
+        task_name=task_name,
+        query=prompt,
+        choices=[f" {c}" for c in line["choices"]],
+        gold_index=line["answer"],
+        instruction=f"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",
+    )
+
+
+CEVAL_STRING = [(t, f"custom|{t.name}|0|1") for t in CEVAL_TASKS]
+_TASKS_STRINGS.extend(CEVAL_STRING)
+_TASKS += CEVAL_TASKS
+
+# ceval
+EARLY_SIGNAL_TASKS = ",".join([t[1] for t in COMMON_SENSE_REASONING_STRING] + [t[1] for t in CEVAL_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,
+}