| |
| |
| """ |
| FinEval 数据准备：CSV -> JSON |
| |
| 来源：HuggingFace SUFE-AIFLM-Lab/FinEval（cc-by-nc-sa-4.0） |
| 对应 NAACL 2025 论文中的「金融学术知识」部分：4,661 道选择题 / 34 学科。 |
| 另外三部分（金融行业知识 1434 / 金融安全知识 1640 / 金融代理 616）官方未开源。 |
| |
| 口径（与 C-Eval 基线对齐，保证两套基准可比）： |
| few-shot 取自 dev（每科恰好 5 题），评测集为 val；test 集官方不公开答案，不使用。 |
| |
| 输出：data/fineval/json/<subject>/{dev,val}.json |
| 字段与 C-Eval 导出保持一致：id / question / A / B / C / D / answer |
| """ |
| import csv |
| import glob |
| import json |
| import os |
|
|
| ROOT = r"D:\ceval-baseline" |
| SRC = os.path.join(ROOT, "data", "fineval", "extracted") |
| OUT = os.path.join(ROOT, "data", "fineval", "json") |
|
|
| |
| CN = { |
| "accounting": "会计", |
| "advanced_financial_accounting": "高级财务会计", |
| "auditing": "审计", |
| "banking_practitioner_qualification_certificate": "银行从业资格", |
| "central_banking": "中央银行学", |
| "certified_management_accountant": "管理会计师", |
| "certified_practising_accountant": "注册会计师", |
| "china_actuary": "中国精算师", |
| "commercial_bank_finance": "商业银行金融", |
| "corporate_finance": "公司金融", |
| "corporate_strategy_and_risk_management": "公司战略与风险管理", |
| "cost_accounting": "成本会计", |
| "econometrics": "计量经济学", |
| "economic_law": "经济法", |
| "finance": "金融学", |
| "financial_engineering": "金融工程", |
| "financial_management": "财务管理", |
| "financial_markets": "金融市场", |
| "fund_qualification_certificate": "基金从业资格", |
| "futures_practitioner_qualification_certificate": "期货从业资格", |
| "insurance": "保险", |
| "intermediate_financial_accounting": "中级财务会计", |
| "international_economics": "国际经济学", |
| "international_finance": "国际金融", |
| "investments": "投资学", |
| "macroeconomics": "宏观经济学", |
| "management_accounting": "管理会计", |
| "microeconomics": "微观经济学", |
| "monetary_finance": "货币金融", |
| "political_economy": "政治经济学", |
| "public_finance": "财政学", |
| "securities_practitioner_qualification_certificate": "证券从业资格", |
| "statistics": "统计学", |
| "tax_law": "税法", |
| } |
|
|
| |
| GROUP = { |
| "finance": ["finance", "corporate_finance", "financial_engineering", "financial_management", |
| "financial_markets", "investments", "international_finance", "monetary_finance", |
| "commercial_bank_finance", "central_banking", "insurance"], |
| "economy": ["microeconomics", "macroeconomics", "econometrics", "international_economics", |
| "political_economy", "public_finance", "statistics", "economic_law"], |
| "accounting": ["accounting", "advanced_financial_accounting", "intermediate_financial_accounting", |
| "auditing", "cost_accounting", "management_accounting"], |
| "certificate": ["certified_practising_accountant", "certified_management_accountant", |
| "china_actuary", "banking_practitioner_qualification_certificate", |
| "securities_practitioner_qualification_certificate", |
| "fund_qualification_certificate", |
| "futures_practitioner_qualification_certificate", |
| "corporate_strategy_and_risk_management", "tax_law"], |
| } |
| SUBJ_GROUP = {s: g for g, ss in GROUP.items() for s in ss} |
|
|
|
|
| def load_split(split): |
| out = {} |
| for f in sorted(glob.glob(os.path.join(SRC, split, "*.csv"))): |
| subj = os.path.basename(f)[: -len(f"_{split}.csv")] |
| rows = [] |
| with open(f, encoding="utf-8-sig") as fh: |
| for row in csv.DictReader(fh): |
| if row.get("answer") not in list("ABCD"): |
| continue |
| |
| clean = lambda s: (s or "").replace("$\\%$", "%").replace("\\%", "%").strip() |
| rows.append({ |
| "id": f"{subj}_{row['id']}", |
| "question": (row.get("question") or "").strip(), |
| "A": clean(row.get("A")), |
| "B": clean(row.get("B")), |
| "C": clean(row.get("C")), |
| "D": clean(row.get("D")), |
| "answer": row["answer"], |
| }) |
| out[subj] = rows |
| return out |
|
|
|
|
| def main(): |
| dev = load_split("dev") |
| val = load_split("val") |
| os.makedirs(OUT, exist_ok=True) |
|
|
| tot_dev = tot_val = 0 |
| summary = [] |
| for subj in sorted(dev): |
| if subj not in val: |
| print("!! val 缺少学科:", subj) |
| continue |
| d, v = dev[subj], val[subj] |
| os.makedirs(os.path.join(OUT, subj), exist_ok=True) |
| with open(os.path.join(OUT, subj, "dev.json"), "w", encoding="utf-8") as f: |
| json.dump(d, f, ensure_ascii=False, indent=1) |
| with open(os.path.join(OUT, subj, "val.json"), "w", encoding="utf-8") as f: |
| json.dump(v, f, ensure_ascii=False, indent=1) |
| tot_dev += len(d) |
| tot_val += len(v) |
| summary.append({ |
| "subject": subj, "subject_cn": CN.get(subj, subj), |
| "group": SUBJ_GROUP.get(subj, "?"), |
| "n_dev": len(d), "n_val": len(v), |
| }) |
|
|
| with open(os.path.join(ROOT, "data", "fineval", "inventory.json"), "w", encoding="utf-8") as f: |
| json.dump({"n_subjects": len(summary), "n_dev": tot_dev, "n_val": tot_val, |
| "subjects": summary}, f, ensure_ascii=False, indent=2) |
|
|
| print(f"学科数 {len(summary)} | dev {tot_dev} 题 | val {tot_val} 题") |
| |
| miss = [s["subject"] for s in summary if s["subject"] not in CN] |
| if miss: |
| print("!! 缺中文名:", miss) |
| |
| import collections |
| c = collections.Counter(s["group"] for s in summary) |
| print("按大类:", dict(c)) |
| print("每科 dev 题数分布:", sorted({s["n_dev"] for s in summary})) |
| print("输出 ->", OUT) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
