| """ |
| Create HuggingFace dataset from scraped news articles. |
| """ |
|
|
| import json |
| from pathlib import Path |
| from datasets import Dataset, DatasetDict |
|
|
| DATA_DIR = Path(__file__).parent.parent / "data" |
| OUTPUT_DIR = Path(__file__).parent.parent / "dataset" |
|
|
|
|
| def load_articles(data_dir: Path) -> list[dict]: |
| """Load articles from JSON files in data directory.""" |
| articles = [] |
| for json_file in data_dir.glob("**/*.json"): |
| with open(json_file, "r", encoding="utf-8") as f: |
| data = json.load(f) |
| if isinstance(data, list): |
| articles.extend(data) |
| else: |
| articles.append(data) |
| return articles |
|
|
|
|
| def main(): |
| OUTPUT_DIR.mkdir(parents=True, exist_ok=True) |
|
|
| |
| all_articles = load_articles(DATA_DIR) |
| print(f"Loaded {len(all_articles)} articles total") |
|
|
| print(f"\nTotal articles: {len(all_articles)}") |
|
|
| |
| dataset_records = [] |
| for article in all_articles: |
| record = { |
| "source": article.get("source", ""), |
| "url": article.get("url", ""), |
| "category": article.get("category", ""), |
| "content": article.get("content", ""), |
| "title": article.get("title", ""), |
| "description": article.get("description", ""), |
| "publish_date": article.get("publish_date", ""), |
| } |
| dataset_records.append(record) |
|
|
| |
| dataset = Dataset.from_list(dataset_records) |
|
|
| |
| split_dataset = dataset.train_test_split(test_size=0.1, seed=42) |
|
|
| dataset_dict = DatasetDict({ |
| "train": split_dataset["train"], |
| "test": split_dataset["test"] |
| }) |
|
|
| |
| dataset_dict.save_to_disk(OUTPUT_DIR / "UVN-1") |
|
|
| |
| print("\n=== Dataset Statistics ===") |
| print(f"Train samples: {len(dataset_dict['train'])}") |
| print(f"Test samples: {len(dataset_dict['test'])}") |
|
|
| |
| print("\n=== Category Distribution ===") |
| categories = {} |
| for record in dataset_records: |
| cat = record["category"] |
| categories[cat] = categories.get(cat, 0) + 1 |
|
|
| for cat, count in sorted(categories.items(), key=lambda x: -x[1]): |
| print(f" {cat}: {count}") |
|
|
| |
| print("\n=== Source Distribution ===") |
| sources = {} |
| for record in dataset_records: |
| src = record["source"] |
| sources[src] = sources.get(src, 0) + 1 |
|
|
| for src, count in sorted(sources.items(), key=lambda x: -x[1]): |
| print(f" {src}: {count}") |
|
|
| print(f"\nDataset saved to: {OUTPUT_DIR / 'UVN-1'}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
