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Benchmark Overview

Benchmarks provide standardized evaluation metrics for comparing model performance across different training runs and configurations. Use benchmarks to evaluate, compare, and select the best models.

What are Benchmarks?​

Benchmarks in NeoSpace are standardized evaluation configurations that define how models should be evaluated. They specify datasets, metrics, and task types for consistent model comparison.

Key Characteristics:

  • Standardized Evaluation: Consistent evaluation across models
  • Multiple Metrics: Support for various evaluation metrics
  • Task Types: Classification, regression, text generation
  • Dataset Consistency: Ensure benchmark datasets remain consistent
  • Performance Tracking: Track model performance over time

Benchmark Overview

Why Use Benchmarks?​

Benchmarks are essential for:

  • Model Comparison: Compare models fairly and consistently
  • Performance Evaluation: Evaluate model performance objectively
  • Model Selection: Select best performing models
  • Reproducibility: Ensure reproducible evaluation results
  • Tracking Progress: Track performance improvements over time

Use Cases​

Benchmarks are used for:

  • Model Evaluation: Evaluate trained models
  • Model Comparison: Compare different model versions
  • A/B Testing: Compare different model architectures
  • Performance Monitoring: Monitor model performance over time
  • Research: Conduct model research and experiments

Next Steps​