Dataset Evaluation

Dataset evaluation measures the quality, diversity, and suitability of a dataset before it is used for training or benchmarking models.

  Evaluation Dimensions

  Sample Evaluation Report

  Design Trade-offs

• Aggressive filtering boosts cleanliness but may reduce minority dialect coverage.
• Over-deduplication risks discarding legitimate paraphrases needed for robustness.

  Current Trends (2025)

• Automated attribution scanners tag CC-BY passages to satisfy legal notices.
• Large-scale "Contrastive Data Audits" compare candidate datasets to web snapshots for leakage detection.
• Benchmarks like DataComp v2 score datasets on downstream finetuning wins per token.

  Implementation Tips

1. Run evaluation as part of CI when ingesting new data.
2. Store raw metrics as JSON for future regression comparison.
3. Visualize class imbalance with heatmaps to communicate issues to annotators.