Token-level F1 (Token-level F1 Score) is an important evaluation metric for question-answering systems in the field of natural language processing. Token-level F1 calculates the harmonic mean of precision and recall for answers based on token overlap, allowing for partial matches and thus being more lenient. It is typically reported as a percentage or a score between 0 and 1. This metric is widely used in relevant benchmarks and papers, effectively reflecting model performance in this dimension. Higher values are better. It helps researchers and engineers quantify the actual performance differences of models in question-answering scenarios, facilitating the horizontal comparison of different methods or model versions.