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""" |
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Custom evaluation tasks for lighteval |
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Do note that we ran the evals with `max_samples=1000` to speed up large evals. |
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Most custom prompt changes were in an attempt to improve signal for small models in general. |
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This file generally creates just a TASKS_TABLE and TASKS_GROUPS which are then imported by LightEval. |
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Example usage (lighteval_tasks.py is the path to this file): |
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=================== |
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accelerate launch --num_processes=1 lighteval/run_evals_accelerate.py --model_args="pretrained=HuggingFaceFW/ablation-model-fineweb-edu" \ |
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--custom_tasks "lighteval_tasks.py" --output_dir [OUTPUTPATH] --max_samples 1000 \ |
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--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" |
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=================== |
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More info here: https://github.com/huggingface/lighteval?tab=readme-ov-file#evaluate-a-model-on-extended-community-or-custom-tasks |
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For more info on differences between MMLU implementations: https://huggingface.co/blog/open-llm-leaderboard-mmlu#1001-flavors-of-mmlu |
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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. |
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Instead, we use the full MMLU answer as the target. |
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""" |
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import re |
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from typing import List, Tuple |
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from lighteval.metrics import Metrics |
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from lighteval.tasks.lighteval_task import LightevalTaskConfig |
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from lighteval.tasks.requests import Doc |
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from lighteval.tasks.tasks_prompt_formatting import LETTER_INDICES |
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_TASKS_STRINGS: List[Tuple[LightevalTaskConfig, str]] = [] |
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_TASKS: List[LightevalTaskConfig] = [] |
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COMMON_SENSE_REASONING_TASKS = [ |
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LightevalTaskConfig( |
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name="hellaswag", |
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prompt_function="hellaswag_prompt", |
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hf_repo="hellaswag", |
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hf_subset="default", |
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metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], |
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), |
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LightevalTaskConfig( |
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name="winogrande", |
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prompt_function="winogrande", |
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hf_repo="winogrande", |
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hf_subset="winogrande_xl", |
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metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], |
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), |
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LightevalTaskConfig( |
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name="piqa", |
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prompt_function="piqa_harness", |
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hf_repo="piqa", |
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hf_subset="plain_text", |
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metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], |
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), |
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LightevalTaskConfig( |
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name="siqa", |
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prompt_function="siqa_prompt", |
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hf_repo="lighteval/siqa", |
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hf_subset="default", |
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hf_avail_splits=["train", "validation"], |
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metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], |
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), |
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LightevalTaskConfig( |
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name="openbookqa", |
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prompt_function="openbookqa", |
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hf_repo="openbookqa", |
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hf_subset="main", |
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metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], |
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), |
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LightevalTaskConfig( |
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name="arc:easy", |
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prompt_function="arc", |
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hf_repo="ai2_arc", |
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hf_subset="ARC-Easy", |
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evaluation_splits=["test"], |
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generation_size=1, |
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metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], |
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), |
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LightevalTaskConfig( |
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name="arc:challenge", |
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prompt_function="arc", |
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hf_repo="ai2_arc", |
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hf_subset="ARC-Challenge", |
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evaluation_splits=["test"], |
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generation_size=1, |
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metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], |
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), |
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LightevalTaskConfig( |
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name="commonsense_qa", |
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prompt_function="commonsense_qa_prompt", |
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hf_repo="commonsense_qa", |
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hf_subset="default", |
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metric=["loglikelihood_acc", "loglikelihood_acc_norm_nospace"], |
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), |
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] |
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def commonsense_qa_prompt(line, task_name: str = None): |
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return Doc( |
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task_name=task_name, |
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query=line["question"], |
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choices=[f" {c}" for c in line["choices"]["text"]], |
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gold_index=LETTER_INDICES.index(line["answerKey"].strip()), |
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instruction="", |
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) |
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def siqa_prompt(line, task_name: str = None): |
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return Doc( |
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task_name=task_name, |
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query=line["context"] + " " + line["question"], |
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choices=[f" {c}" for c in [line["answerA"], line["answerB"], line["answerC"]]], |
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gold_index=int(line["label"]) - 1, |
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instruction="", |
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) |
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def hellaswag_prompt(line, task_name: str = None): |
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def preprocess(text): |
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"""Comes from AiHarness""" |
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text = text.replace(" [title]", ". ") |
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text = re.sub("\\[.*?\\]", "", text) |
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text = text.replace(" ", " ") |
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return text |
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ctx = f"{line['ctx_a']} {line['ctx_b'].capitalize()} " |
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return Doc( |
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task_name=task_name, |
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query=preprocess(line["activity_label"] + ": " + ctx), |
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choices=[" " + preprocess(ending) for ending in line["endings"]], |
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gold_index=int(line["label"]) if line["label"] != "" else -1, |
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) |
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COMMON_SENSE_REASONING_STRING = [(t, f"custom|{t.name}|0|1") for t in COMMON_SENSE_REASONING_TASKS] |
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_TASKS_STRINGS.extend(COMMON_SENSE_REASONING_STRING) |
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_TASKS += COMMON_SENSE_REASONING_TASKS |
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class CustomMMLUEvaluationTask(LightevalTaskConfig): |
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def __init__( |
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self, |
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name, |
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prompt_function="mmlu_prompt", |
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hf_repo="lighteval/mmlu", |
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hf_subset=None, |
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metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], |
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hf_avail_splits=None, |
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evaluation_splits=["test"], |
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few_shots_split="dev", |
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few_shots_select=None, |
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suite=None, |
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generation_size=-1, |
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stop_sequence=None, |
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output_regex=None, |
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frozen=False, |
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): |
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super().__init__( |
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name=name, |
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prompt_function=prompt_function, |
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hf_repo=hf_repo, |
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hf_subset=hf_subset, |
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metric=metric, |
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hf_avail_splits=hf_avail_splits, |
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evaluation_splits=evaluation_splits, |
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few_shots_split=few_shots_split, |
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few_shots_select=few_shots_select, |
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suite=suite, |
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generation_size=generation_size, |
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stop_sequence=stop_sequence, |
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output_regex=output_regex, |
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frozen=frozen, |
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) |
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MMLU_TASKS = [ |
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CustomMMLUEvaluationTask(name="mmlu:abstract_algebra", hf_subset="abstract_algebra"), |
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CustomMMLUEvaluationTask(name="mmlu:anatomy", hf_subset="anatomy"), |
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CustomMMLUEvaluationTask(name="mmlu:astronomy", hf_subset="astronomy"), |
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CustomMMLUEvaluationTask(name="mmlu:business_ethics", hf_subset="business_ethics"), |
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CustomMMLUEvaluationTask(name="mmlu:clinical_knowledge", hf_subset="clinical_knowledge"), |
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CustomMMLUEvaluationTask(name="mmlu:college_biology", hf_subset="college_biology"), |
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CustomMMLUEvaluationTask(name="mmlu:college_chemistry", hf_subset="college_chemistry"), |
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CustomMMLUEvaluationTask(name="mmlu:college_computer_science", hf_subset="college_computer_science"), |
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CustomMMLUEvaluationTask(name="mmlu:college_mathematics", hf_subset="college_mathematics"), |
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CustomMMLUEvaluationTask(name="mmlu:college_medicine", hf_subset="college_medicine"), |
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CustomMMLUEvaluationTask(name="mmlu:college_physics", hf_subset="college_physics"), |
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CustomMMLUEvaluationTask(name="mmlu:computer_security", hf_subset="computer_security"), |
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CustomMMLUEvaluationTask(name="mmlu:conceptual_physics", hf_subset="conceptual_physics"), |
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CustomMMLUEvaluationTask(name="mmlu:econometrics", hf_subset="econometrics"), |
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CustomMMLUEvaluationTask(name="mmlu:electrical_engineering", hf_subset="electrical_engineering"), |
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CustomMMLUEvaluationTask(name="mmlu:elementary_mathematics", hf_subset="elementary_mathematics"), |
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CustomMMLUEvaluationTask(name="mmlu:formal_logic", hf_subset="formal_logic"), |
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CustomMMLUEvaluationTask(name="mmlu:global_facts", hf_subset="global_facts"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_biology", hf_subset="high_school_biology"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_chemistry", hf_subset="high_school_chemistry"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_computer_science", hf_subset="high_school_computer_science"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_european_history", hf_subset="high_school_european_history"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_geography", hf_subset="high_school_geography"), |
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CustomMMLUEvaluationTask( |
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name="mmlu:high_school_government_and_politics", hf_subset="high_school_government_and_politics" |
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), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_macroeconomics", hf_subset="high_school_macroeconomics"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_mathematics", hf_subset="high_school_mathematics"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_microeconomics", hf_subset="high_school_microeconomics"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_physics", hf_subset="high_school_physics"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_psychology", hf_subset="high_school_psychology"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_statistics", hf_subset="high_school_statistics"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_us_history", hf_subset="high_school_us_history"), |
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CustomMMLUEvaluationTask(name="mmlu:high_school_world_history", hf_subset="high_school_world_history"), |
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CustomMMLUEvaluationTask(name="mmlu:human_aging", hf_subset="human_aging"), |
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CustomMMLUEvaluationTask(name="mmlu:human_sexuality", hf_subset="human_sexuality"), |
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CustomMMLUEvaluationTask(name="mmlu:international_law", hf_subset="international_law"), |
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CustomMMLUEvaluationTask(name="mmlu:jurisprudence", hf_subset="jurisprudence"), |
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CustomMMLUEvaluationTask(name="mmlu:logical_fallacies", hf_subset="logical_fallacies"), |
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CustomMMLUEvaluationTask(name="mmlu:machine_learning", hf_subset="machine_learning"), |
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CustomMMLUEvaluationTask(name="mmlu:management", hf_subset="management"), |
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CustomMMLUEvaluationTask(name="mmlu:marketing", hf_subset="marketing"), |
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CustomMMLUEvaluationTask(name="mmlu:medical_genetics", hf_subset="medical_genetics"), |
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CustomMMLUEvaluationTask(name="mmlu:miscellaneous", hf_subset="miscellaneous"), |
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CustomMMLUEvaluationTask(name="mmlu:moral_disputes", hf_subset="moral_disputes"), |
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CustomMMLUEvaluationTask(name="mmlu:moral_scenarios", hf_subset="moral_scenarios"), |
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CustomMMLUEvaluationTask(name="mmlu:nutrition", hf_subset="nutrition"), |
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CustomMMLUEvaluationTask(name="mmlu:philosophy", hf_subset="philosophy"), |
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CustomMMLUEvaluationTask(name="mmlu:prehistory", hf_subset="prehistory"), |
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CustomMMLUEvaluationTask(name="mmlu:professional_accounting", hf_subset="professional_accounting"), |
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CustomMMLUEvaluationTask(name="mmlu:professional_law", hf_subset="professional_law"), |
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CustomMMLUEvaluationTask(name="mmlu:professional_medicine", hf_subset="professional_medicine"), |
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CustomMMLUEvaluationTask(name="mmlu:professional_psychology", hf_subset="professional_psychology"), |
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CustomMMLUEvaluationTask(name="mmlu:public_relations", hf_subset="public_relations"), |
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CustomMMLUEvaluationTask(name="mmlu:security_studies", hf_subset="security_studies"), |
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CustomMMLUEvaluationTask(name="mmlu:sociology", hf_subset="sociology"), |
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CustomMMLUEvaluationTask(name="mmlu:us_foreign_policy", hf_subset="us_foreign_policy"), |
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CustomMMLUEvaluationTask(name="mmlu:virology", hf_subset="virology"), |
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CustomMMLUEvaluationTask(name="mmlu:world_religions", hf_subset="world_religions"), |
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] |
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def mmlu_prompt(line, task_name: str = None): |
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"""MMLU prompt without letters""" |
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topic = line["subject"] |
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prompt = f"The following are questions about {topic.replace('_', ' ')}.\nQuestion: " |
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prompt += line["question"] + "\nAnswer:" |
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return Doc( |
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task_name=task_name, |
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query=prompt, |
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choices=[f" {c}" for c in line["choices"]], |
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gold_index=line["answer"], |
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instruction=f"The following are questions about {topic.replace('_', ' ')}.\n", |
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) |
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MMLU_STRING = [(t, f"custom|{t.name}|0|1") for t in MMLU_TASKS] |
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_TASKS_STRINGS.extend(MMLU_STRING) |
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_TASKS += MMLU_TASKS |
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EARLY_SIGNAL_TASKS = ",".join([t[1] for t in COMMON_SENSE_REASONING_STRING] + [t[1] for t in MMLU_STRING]) |
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TASKS_TABLE = [task.as_dict() for task in _TASKS] |
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TASKS_GROUPS = { |
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"early-signal": EARLY_SIGNAL_TASKS, |
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} |
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