File size: 3,007 Bytes
dd0b07f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
from dataclasses import dataclass
from typing import Any, Final
from llmdataparser.base_parser import HuggingFaceDatasetParser, HuggingFaceParseEntry
from llmdataparser.prompts import TW_LEGAL_SYSTEM_PROMPT
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,
prompt: 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(
prompt=prompt,
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"]
_default_system_prompt = TW_LEGAL_SYSTEM_PROMPT
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"]
prompt = f"{self._system_prompt}\nQuestion: {raw_question}\n{choices}\nAnswer:"
return TWLegalParseEntry.create(
prompt=prompt,
answer=raw_answer,
raw_question=raw_question,
raw_choices=raw_choices,
raw_answer=raw_answer,
task_name=task,
)
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.raw_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}")
|