标题： 探索微调模型在使用DistilBERT进行文本分类中的变异性 显示英文标题

标题： Exploring Variability in Fine-Tuned Models for Text Classification with DistilBERT

摘要： 本研究评估了使用DistilBERT模型进行文本分类的微调策略，特别是distilbert-base-uncased-finetuned-sst-2-english变体。通过结构化的实验，我们考察了学习率、批量大小和轮数等超参数对准确率、F1分数和损失的影响。多项式回归分析捕捉了这些超参数的基础和增量影响，重点关注相对于基准模型的微调调整。结果揭示了由于超参数配置导致的指标变化，显示了性能指标之间的权衡。例如，较高的学习率在相对分析中减少了损失（p=0.027），但对准确率提升构成挑战。同时，批量大小在绝对回归中显著影响准确率和F1分数（p=0.028和p=0.005），但对损失优化的影响有限（p=0.170）。轮数和批量大小之间的交互作用最大化了F1分数（p=0.001），强调了超参数相互作用的重要性。这些发现突显了需要针对非线性超参数交互的微调策略，以在不同指标间平衡性能。这种变化和指标权衡适用于超越文本分类的任务，包括自然语言处理和计算机视觉。这项分析为大型语言模型的微调策略提供了依据，并促进了更广泛模型适用性的自适应设计。 显示英文摘要

摘要： This study evaluates fine-tuning strategies for text classification using the DistilBERT model, specifically the distilbert-base-uncased-finetuned-sst-2-english variant. Through structured experiments, we examine the influence of hyperparameters such as learning rate, batch size, and epochs on accuracy, F1-score, and loss. Polynomial regression analyses capture foundational and incremental impacts of these hyperparameters, focusing on fine-tuning adjustments relative to a baseline model. Results reveal variability in metrics due to hyperparameter configurations, showing trade-offs among performance metrics. For example, a higher learning rate reduces loss in relative analysis (p=0.027) but challenges accuracy improvements. Meanwhile, batch size significantly impacts accuracy and F1-score in absolute regression (p=0.028 and p=0.005) but has limited influence on loss optimization (p=0.170). The interaction between epochs and batch size maximizes F1-score (p=0.001), underscoring the importance of hyperparameter interplay. These findings highlight the need for fine-tuning strategies addressing non-linear hyperparameter interactions to balance performance across metrics. Such variability and metric trade-offs are relevant for tasks beyond text classification, including NLP and computer vision. This analysis informs fine-tuning strategies for large language models and promotes adaptive designs for broader model applicability.