Hugging Face Evaluation Resources

Hugging Face provides a comprehensive set of resources for evaluating machine learning models. Below, you will find a list of evaluation metrics and tools that are commonly used in the field of natural language processing.

Common Evaluation Metrics

• Accuracy: The proportion of correct predictions out of the total number of predictions.
• Precision: The proportion of true positives out of the total number of positive predictions.
• Recall: The proportion of true positives out of the total number of actual positives.
• F1 Score: The harmonic mean of precision and recall.
• ROUGE: Recurrence Optimization Using a Generalized Evaluation Measure, commonly used for evaluating text summarization and machine translation.

Evaluation Tools

• Hugging Face Transformers: This library provides a wide range of pre-trained models and evaluation metrics for various NLP tasks.
• Hugging Face Datasets: A repository of datasets for NLP research and development.
• Hugging Face Metrics: A set of evaluation metrics for NLP tasks.

For more information on Hugging Face evaluation resources, please visit our Evaluation Documentation.

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