| | from datasets import load_dataset |
| |
|
| |
|
| | def display_dataset_statistics(dataset): |
| | """ |
| | Display overall statistics about the dataset. |
| | |
| | Args: |
| | dataset: A HuggingFace Dataset object. |
| | """ |
| | print("\n" + "=" * 50) |
| | print(f"{'DATASET STATISTICS':^50}") |
| | print("=" * 50) |
| |
|
| | print(f"\n📊 Number of entries: {len(dataset):,}") |
| |
|
| | |
| | df = dataset.to_pandas() |
| |
|
| | |
| | if "status" in df.columns: |
| | print("\n" + "-" * 30) |
| | print("📝 PRESENTATION TYPES") |
| | print("-" * 30) |
| | status_counts = df["status"].value_counts() |
| | for status, count in status_counts.items(): |
| | print(f" • {status}: {count:,} ({count / len(df) * 100:.1f}%)") |
| |
|
| | |
| | if "venue" in df.columns: |
| | print("\n" + "-" * 30) |
| | print("🏢 VENUES") |
| | print("-" * 30) |
| | venue_counts = df["venue"].value_counts() |
| | for venue, count in venue_counts.items(): |
| | print(f" • {venue}: {count:,} ({count / len(df) * 100:.1f}%)") |
| |
|
| | |
| | if "primary_area" in df.columns: |
| | print("\n" + "-" * 30) |
| | print("🔬 TOP 10 PRIMARY RESEARCH AREAS") |
| | print("-" * 30) |
| | area_counts = df["primary_area"].value_counts().head(10) |
| | for area, count in area_counts.items(): |
| | print(f" • {area}: {count:,} ({count / len(df) * 100:.1f}%)") |
| |
|
| |
|
| | def display_sample_entries(dataset, n=3): |
| | """ |
| | Display sample entries from the dataset. |
| | |
| | Args: |
| | dataset: A HuggingFace Dataset object. |
| | n: Number of samples to display. |
| | """ |
| | print("\n" + "=" * 50) |
| | print(f"{'SAMPLE ENTRIES':^50}") |
| | print("=" * 50) |
| |
|
| | for i in range(min(n, len(dataset))): |
| | print(f"\n📄 SAMPLE {i + 1}") |
| | print("-" * 30) |
| | print(f"🎬 Video file: video/{dataset[i].get('video_file', 'N/A')}") |
| | print(f"📝 Title: {dataset[i].get('title', 'N/A')}") |
| | print(f"💡 TL;DR: {dataset[i].get('tldr', 'N/A')}") |
| | print(f"🔬 Primary area: {dataset[i].get('primary_area', 'N/A')}") |
| | print(f"🏷️ Keywords: {dataset[i].get('keywords', 'N/A')}") |
| | print("=" * 50) |
| |
|
| |
|
| | if __name__ == "__main__": |
| | |
| | dataset = load_dataset("vivianchen98/LearningPaper24", data_files="metadata/catalog.jsonl", split="train") |
| | print(f"Successfully loaded LearningPaper24 dataset with {len(dataset)} entries.") |
| |
|
| | |
| | display_dataset_statistics(dataset) |
| |
|
| | |
| | display_sample_entries(dataset) |
| |
|