| import json | |
| import os | |
| from typing import Dict, Any, List | |
| OPTION_KEYS = ["A", "B", "C", "D", "E", "F"] | |
| def normalize_option(option: Dict[str, Any]) -> Dict[str, str]: | |
| new_option = {} | |
| option = option or {} | |
| for k in OPTION_KEYS: | |
| new_option[k] = str(option.get(k, "")).strip() | |
| return new_option | |
| def normalize_item(item: Dict[str, Any], idx: int) -> Dict[str, Any]: | |
| return { | |
| "id": item.get("id", idx), | |
| "exam_type": item.get("exam_type", ""), | |
| "exam_class": item.get("exam_class", ""), | |
| "exam_subject": item.get("exam_subject", ""), | |
| "question": item.get("question", ""), | |
| "answer": item.get("answer", ""), | |
| "explanation": item.get("explanation", ""), | |
| "question_type": item.get("question_type", ""), | |
| "option": normalize_option(item.get("option")), | |
| } | |
| def clean_json(input_path: str) -> None: | |
| with open(input_path, "r", encoding="utf-8") as f: | |
| data: List[Dict[str, Any]] = json.load(f) | |
| cleaned = [] | |
| for idx, item in enumerate(data, start=1): | |
| cleaned.append(normalize_item(item, idx)) | |
| base, ext = os.path.splitext(input_path) | |
| output_path = f"{base}-clean{ext}" | |
| with open(output_path, "w", encoding="utf-8") as f: | |
| json.dump(cleaned, f, ensure_ascii=False, indent=2) | |
| print(f"[OK] {output_path} ({len(cleaned)} samples)") | |
| if __name__ == "__main__": | |
| input_files = [ | |
| "CMB-Exam/CMB-train/CMB-train-merge.json", | |
| "CMB-Exam/CMB-val/CMB-val-merge.json", | |
| "CMB-Exam/CMB-test/CMB-test-choice-question-merge.json", | |
| ] | |
| for path in input_files: | |
| clean_json(path) | |