We've just released what might be the most comprehensive documentation of AI data quality evaluation metrics available. This covers everything from pre-training data assessment to multimodal evaluation.

What's included:

50+ evaluation metrics across text, image, and multimodal data Academic citations for every metric (RedPajama, CLIP, NIMA, etc.) Rule-based and LLM-based evaluation approaches Practical usage examples and API documentation Key categories:

Text Quality: Completeness, Fluency, Relevance, Effectiveness Image Quality: Clarity, Similarity, Validity Security: Political sensitivity, prohibited content, harmful information Classification: Topic categorization, content classification This is particularly useful for:

Data scientists working on model training Researchers needing standardized evaluation frameworks Anyone dealing with large-scale data quality assessment The documentation includes detailed academic references and practical implementation examples. All open source and ready to use.

Metrics Link: https://github.com/MigoXLab/dingo/blob/dev/docs/metrics.md