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from dataclasses import dataclass
from typing import Any, Final
from llmdataparser.base_parser import (
DatasetDescription,
EvaluationMetric,
HuggingFaceDatasetParser,
HuggingFaceParseEntry,
)
TW_LEGAL_VALID_ANSWERS: Final[set[str]] = {"A", "B", "C", "D"}
TW_LEGAL_VALID_ANSWER_STR: Final[str] = ", ".join(sorted(TW_LEGAL_VALID_ANSWERS))
@dataclass(frozen=True, kw_only=True, slots=True)
class TWLegalParseEntry(HuggingFaceParseEntry):
"""Custom entry class for Taiwan Legal Benchmark, with fields specific to this dataset parser."""
raw_choices: list[str]
@classmethod
def create(
cls,
question: str,
answer: str,
raw_question: str,
raw_choices: list[str],
raw_answer: str,
task_name: str,
) -> "TWLegalParseEntry":
if answer not in TW_LEGAL_VALID_ANSWERS:
raise ValueError(
f"Invalid answer_letter '{answer}'; must be one of {TW_LEGAL_VALID_ANSWER_STR}"
)
return cls(
question=question,
answer=answer,
raw_question=raw_question,
raw_answer=raw_answer,
raw_choices=raw_choices,
task_name=task_name,
)
class TWLegalDatasetParser(HuggingFaceDatasetParser[TWLegalParseEntry]):
"""Parser for the Taiwan Legal Benchmark dataset."""
_data_source = "lianghsun/tw-legal-benchmark-v1"
_default_task = "default"
_task_names = ["default"]
def process_entry(
self, row: dict[str, Any], task_name: str | None = None, **kwargs: Any
) -> TWLegalParseEntry:
"""Process a single Taiwan Legal Benchmark entry."""
# Extract choices in order
task = task_name or self._get_current_task(row)
raw_choices = [row["A"], row["B"], row["C"], row["D"]]
choices = "\n".join(
f"{chr(65 + i)}. {choice}" for i, choice in enumerate(raw_choices)
)
raw_question = row["question"]
raw_answer = row["answer"]
question = f"Question: {raw_question}\n{choices}\nAnswer:"
return TWLegalParseEntry.create(
question=question,
answer=raw_answer,
raw_question=raw_question,
raw_choices=raw_choices,
raw_answer=raw_answer,
task_name=task,
)
def get_dataset_description(self) -> DatasetDescription:
"""Returns description of the Taiwan Legal Benchmark dataset."""
return DatasetDescription.create(
name="Taiwan Legal Benchmark",
language="Traditional Chinese",
purpose="Evaluate models on Taiwan-specific legal knowledge and understanding",
source="Taiwan Bar Examination questions",
category=["Taiwan", "General Knowledge and Reasoning", "Legal"],
format="Multiple choice questions (A/B/C/D)",
characteristics=(
"Contains questions from Taiwan's bar examination, testing understanding "
"of Taiwan's legal system, terminology, and concepts"
),
citation="""
url={https://huggingface.co/datasets/lianghsun/tw-legal-benchmark-v1}
""",
)
def get_evaluation_metrics(self) -> list[EvaluationMetric]:
"""Returns recommended evaluation metrics for Taiwan Legal Benchmark."""
return [
EvaluationMetric.create(
name="accuracy",
type="classification",
description="Overall percentage of correctly answered legal questions",
implementation="datasets.load_metric('accuracy')",
primary=True,
),
]
if __name__ == "__main__":
# Example usage
parser = TWLegalDatasetParser()
parser.load()
parser.parse()
# Get parsed data with correct type
parsed_data = parser.get_parsed_data
# Print example entry
if parsed_data:
example = parsed_data[0]
print("\nExample parsed entry:")
print(f"Question: {example.question}")
print("Choices:")
for i, choice in enumerate(example.raw_choices):
print(f"{chr(65 + i)}. {choice}")
print(f"Correct Answer: {example.answer}")
print(f"Task Name: {example.task_name}")
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