Instructions to use cvelist/spidder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvelist/spidder with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cvelist/spidder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Delete lighteval_tasks.py
Browse files- lighteval_tasks.py +0 -251
lighteval_tasks.py
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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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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.strip()
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# NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
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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, # -1 for test
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# "metric": "choices_loglikelihood",
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)
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# 0 short for common sense
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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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## MMLU ##
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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_single_token],
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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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# common sense reasoning + mmlu
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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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# Convert to dict for lighteval
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TASKS_TABLE = [task.as_dict() for task in _TASKS]
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# You can have a few pre-organised groups of tasks
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TASKS_GROUPS = {
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"early-signal": EARLY_SIGNAL_TASKS,
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}
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