Upload scripts/create_dataset.py with huggingface_hub

scripts/create_dataset.py

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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()