Datasets:
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"""
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)
# Load all articles
all_articles = load_articles(DATA_DIR)
print(f"Loaded {len(all_articles)} articles total")
print(f"\nTotal articles: {len(all_articles)}")
# Create dataset with required fields
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)
# Create HuggingFace dataset
dataset = Dataset.from_list(dataset_records)
# Split into train/test (90/10)
split_dataset = dataset.train_test_split(test_size=0.1, seed=42)
dataset_dict = DatasetDict({
"train": split_dataset["train"],
"test": split_dataset["test"]
})
# Save dataset
dataset_dict.save_to_disk(OUTPUT_DIR / "UVN-1")
# Print statistics
print("\n=== Dataset Statistics ===")
print(f"Train samples: {len(dataset_dict['train'])}")
print(f"Test samples: {len(dataset_dict['test'])}")
# Category distribution
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}")
# Source distribution
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()
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