Hugging Face provides a suite of metrics for machine learning models. These metrics are essential for evaluating the performance of models and understanding their capabilities. Below, we'll explore some of the key metrics offered by Hugging Face.

Common Metrics

  • Accuracy: The ratio of correctly predicted observations to the total observations.
  • Precision: The ratio of correctly predicted positive observations to the total predicted positive observations.
  • Recall: The ratio of correctly predicted positive observations to the all observations in actual class.
  • F1 Score: The weighted average of Precision and Recall, useful when the class distribution is uneven.

How to Access Metrics

To access the metrics on Hugging Face, you can use the following URL: Hugging Face Metrics.

Further Reading

For more detailed information on Hugging Face metrics and their applications, you can check out the Hugging Face Documentation.

Machine Learning Metrics

Would you like to learn more about specific metrics or their applications? Check out our Machine Learning Metrics Guide.