import argparse import re from pathlib import Path import datasets DATASET_CONFIGS = { "writingprompts": { "hf_id": "writingprompts", "prompt_field": "prompt", "response_field": "story", }, "tinystories": { "hf_id": "roneneldan/TinyStories", "prompt_field": None, "response_field": "text", }, "redditjokes": { "hf_id": None, "prompt_field": "title", "response_field": "body", }, } def ensure_dir(path: Path) -> None: path.mkdir(parents=True, exist_ok=True) def get_columns(dataset): if isinstance(dataset, dict): for split in ("train", "validation", "test"): if split in dataset: return dataset[split].column_names first_split = next(iter(dataset.keys())) return dataset[first_split].column_names return dataset.column_names def resolve_field(columns, field_name, fallbacks): if field_name and field_name in columns: return field_name for candidate in fallbacks: if candidate in columns: return candidate return None def make_splits(dataset, seed: int): if isinstance(dataset, dict): if "train" in dataset and "validation" in dataset and "test" in dataset: return dataset["train"], dataset["validation"], dataset["test"] if "train" in dataset and "test" in dataset: split = dataset["train"].train_test_split(test_size=0.2, seed=seed) val_test = split["test"].train_test_split(test_size=0.5, seed=seed) return split["train"], val_test["train"], val_test["test"] base_split = dataset.get("train") or dataset[next(iter(dataset.keys()))] split = base_split.train_test_split(test_size=0.2, seed=seed) val_test = split["test"].train_test_split(test_size=0.5, seed=seed) return split["train"], val_test["train"], val_test["test"] if "train" in dataset and "validation" in dataset and "test" in dataset: return dataset["train"], dataset["validation"], dataset["test"] if "train" in dataset and "test" in dataset: split = dataset["train"].train_test_split(test_size=0.2, seed=seed) val_test = split["test"].train_test_split(test_size=0.5, seed=seed) return split["train"], val_test["train"], val_test["test"] split = dataset.train_test_split(test_size=0.2, seed=seed) val_test = split["test"].train_test_split(test_size=0.5, seed=seed) return split["train"], val_test["train"], val_test["test"] def clean_text(text): if text is None: return "" cleaned = str(text).replace("\r", " ").replace("\n", " ").strip() cleaned = re.sub(r"\s+", " ", cleaned) return cleaned def should_drop_deleted(text): lowered = text.strip().lower() return lowered in {"[deleted]", "[removed]"} def normalize_records(dataset, prompt_field, response_field, source_name, include_metadata): def _map(example): prompt = clean_text(example[prompt_field]) if prompt_field else "" response = clean_text(example[response_field]) record = { "prompt": prompt.strip(), "response": response.strip(), "source": source_name, } if include_metadata: metadata = {} for key in ("score", "author", "id", "subreddit"): if key in example and example[key] not in (None, ""): metadata[key] = example[key] if metadata: record["metadata"] = metadata return record return dataset.map(_map, remove_columns=dataset.column_names) def main() -> None: parser = argparse.ArgumentParser(description="Download datasets for QuestCrafter.") parser.add_argument( "--dataset", choices=DATASET_CONFIGS.keys(), default="redditjokes", help="Dataset source to download from Hugging Face or local CSV.", ) parser.add_argument( "--output_dir", default="data/raw", help="Directory to save JSONL splits.", ) parser.add_argument( "--local_csv", default=None, help="Path to a local CSV file (required for redditjokes).", ) parser.add_argument( "--prompt_field", default=None, help="Optional prompt column name override.", ) parser.add_argument( "--response_field", default=None, help="Optional response column name override.", ) parser.add_argument( "--min_prompt_chars", type=int, default=5, help="Minimum prompt length (characters). Ignored if prompt field is empty.", ) parser.add_argument( "--max_prompt_chars", type=int, default=300, help="Maximum prompt length (characters). Ignored if prompt field is empty.", ) parser.add_argument( "--min_response_chars", type=int, default=20, help="Minimum response length (characters).", ) parser.add_argument( "--max_response_chars", type=int, default=800, help="Maximum response length (characters).", ) parser.add_argument( "--keep_deleted", action="store_true", help="Keep rows with [deleted]/[removed] responses (default: drop).", ) parser.add_argument( "--no_metadata", action="store_true", help="Disable metadata fields in JSONL output.", ) parser.add_argument("--seed", type=int, default=42, help="Random seed for splits.") args = parser.parse_args() config = DATASET_CONFIGS[args.dataset] if config["hf_id"]: dataset = datasets.load_dataset(config["hf_id"]) else: if not args.local_csv: raise ValueError("For redditjokes, you must provide --local_csv.") csv_path = Path(args.local_csv) if not csv_path.exists(): raise FileNotFoundError(f"CSV not found: {csv_path}") dataset = datasets.load_dataset("csv", data_files=str(csv_path)) columns = get_columns(dataset) prompt_field = resolve_field( columns, args.prompt_field or config["prompt_field"], ["prompt", "title", "question", "setup", "context"], ) response_field = resolve_field( columns, args.response_field or config["response_field"], ["response", "body", "joke", "Joke", "text", "completion", "answer"], ) if response_field is None: raise ValueError(f"Response column not found in CSV. Columns: {columns}") def is_valid(example): prompt = clean_text(example[prompt_field]) if prompt_field else "" response = clean_text(example[response_field]) if not response: return False if not args.keep_deleted and should_drop_deleted(response): return False if prompt_field: if len(prompt) < args.min_prompt_chars: return False if len(prompt) > args.max_prompt_chars: return False if len(response) < args.min_response_chars: return False if len(response) > args.max_response_chars: return False return True dataset = dataset.filter(is_valid) train, val, test = make_splits(dataset, args.seed) include_metadata = not args.no_metadata train = normalize_records(train, prompt_field, response_field, args.dataset, include_metadata) val = normalize_records(val, prompt_field, response_field, args.dataset, include_metadata) test = normalize_records(test, prompt_field, response_field, args.dataset, include_metadata) output_dir = Path(args.output_dir) / args.dataset ensure_dir(output_dir) train.to_json(output_dir / "train.jsonl", orient="records", lines=True) val.to_json(output_dir / "val.jsonl", orient="records", lines=True) test.to_json(output_dir / "test.jsonl", orient="records", lines=True) print(f"Saved splits to {output_dir}") if __name__ == "__main__": main()