The token-level F1 score is an important evaluation metric for question-answering tasks in the field of natural language processing. It calculates the harmonic mean of precision and recall based on token overlap, tolerates partial matches, and is therefore 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 differences in actual model performance in question-answering scenarios, facilitating horizontal comparisons between different methods or model versions.