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#!/usr/bin/env python3
"""
Example Usage of Napolab Leaderboard Data Management
This script demonstrates how to use the YAML-based data management system.
"""
from data_loader import NapolabDataLoader
from manage_data import validate_yaml_structure, add_dataset, add_benchmark_result, add_model_metadata, export_data
import yaml
def example_usage():
"""Demonstrate the data management functionality."""
print("🚀 Napolab Leaderboard Data Management Example")
print("=" * 50)
# 1. Load existing data
print("\n1. Loading existing data...")
data_loader = NapolabDataLoader()
data = data_loader.data
print(f"✅ Loaded {len(data['datasets'])} datasets")
print(f"✅ Loaded {len(data['model_metadata'])} models")
# 2. Validate the data structure
print("\n2. Validating data structure...")
if validate_yaml_structure(data):
print("✅ Data structure is valid!")
else:
print("❌ Data structure has issues!")
return
# 3. Add a new dataset
print("\n3. Adding a new dataset...")
data = add_dataset(
data=data,
dataset_name="example_dataset",
name="Example Dataset",
description="An example dataset for demonstration",
tasks=["Classification", "Sentiment Analysis"],
url="https://huggingface.co/datasets/example"
)
# 4. Add a new model
print("\n4. Adding a new model...")
data = add_model_metadata(
data=data,
model_name="example-model",
parameters=125000000,
architecture="BERT Large",
base_model="bert-large-uncased",
task="Classification",
huggingface_url="https://huggingface.co/example/model"
)
# 5. Add benchmark results
print("\n5. Adding benchmark results...")
data = add_benchmark_result(
data=data,
dataset_name="example_dataset",
model_name="example-model",
metrics={
"accuracy": 0.89,
"f1": 0.88,
"precision": 0.90,
"recall": 0.87
}
)
# 6. Export the updated data
print("\n6. Exporting updated data...")
export_data(data, "example_updated_data.yaml")
# 7. Demonstrate data access
print("\n7. Demonstrating data access...")
# Get dataset info
dataset_info = data_loader.get_dataset_info("assin")
if dataset_info:
print(f"📊 ASSIN dataset: {dataset_info['name']}")
print(f" Tasks: {', '.join(dataset_info['tasks'])}")
# Get available models for a dataset
models = data_loader.get_available_models_for_dataset("assin")
print(f"🤖 Available models for ASSIN: {len(models)} models")
# Get model info
model_info = data_loader.get_model_info("mdeberta-v3-base-assin-similarity")
if model_info:
print(f"🔧 Model parameters: {model_info['parameters']:,}")
print(f" Architecture: {model_info['architecture']}")
print("\n✅ Example completed successfully!")
print("📁 Check 'example_updated_data.yaml' for the updated data")
def demonstrate_yaml_structure():
"""Show the YAML structure."""
print("\n📋 YAML Data Structure Example:")
print("-" * 30)
example_data = {
'datasets': {
'my_dataset': {
'name': 'My Dataset',
'description': 'A custom dataset',
'tasks': ['Classification'],
'url': 'https://huggingface.co/datasets/my_dataset'
}
},
'benchmark_results': {
'my_dataset': {
'my-model': {
'accuracy': 0.92,
'f1': 0.91
}
}
},
'model_metadata': {
'my-model': {
'parameters': 110000000,
'architecture': 'BERT Base',
'base_model': 'bert-base-uncased',
'task': 'Classification',
'huggingface_url': 'https://huggingface.co/my-model'
}
}
}
print(yaml.dump(example_data, default_flow_style=False, allow_unicode=True))
if __name__ == "__main__":
example_usage()
demonstrate_yaml_structure() |