Datasets:

HuggingFaceFW
/

fineweb

	# 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.
	"""
	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_harness(line, task_name: str = None):
	topic = line["subject"]
	prompt = f"The following are multiple choice questions (with answers) about {topic.replace('_', ' ')}.\n\n"
	prompt += line["question"] + "\n"
	prompt += "".join([f"{key}. {choice}\n" for key, choice in zip(LETTER_INDICES, line["choices"])])
	prompt += "Answer:"

	gold_ix = LETTER_INDICES.index(line["answer"]) if isinstance(line["answer"], str) else line["answer"]
	"__few_shots" in line and line["__few_shots"] is True # We are adding few shots

	return Doc(
	task_name=task_name,
	query=prompt,
	choices=[" A", " B", " C", " D"],
	target_for_fewshot_sorting=[" A", " B", " C", " D"][gold_ix],
	gold_index=gold_ix,
	instruction=f"The following are multiple choice questions (with answers) about {topic.replace('_', ' ')}.\n\n",
	)


	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]
	+ ["lighteval\|sciq\|0\|0"]) # note that we actually do not use sciq to compute the agg score

	# 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,
	}