用精简版BERT在英特尔CPU上实现快速问答，准确率超基线一倍。
Improving QA Efficiency with DistilBERT: Fine-Tuning and Inference on mobile Intel CPUs
- 基于DistilBERT做微调，结合数据增强提升性能
- 单题平均推理仅0.12秒，验证集F1达0.6536
- 适合移动端或资源受限设备的实时问答应用
本研究提出一种面向13代英特尔i7-1355U CPU优化的Transformer问答模型，使用SQuAD v1.1数据集。通过探索性数据分析、数据增强及DistilBERT架构微调，模型在验证集上取得0.6536的F1分数，单题平均推理时间为0.1208秒。相比规则基线（F1: 0.3124）和完整BERT模型，该方法在准确率与计算效率间实现良好平衡，适用于资源受限系统的实时问答应用。研究系统评估了数据增强策略与超参数配置，为基于CPU的Transformer模型优化提供实用参考。
原文摘要 · Abstract (English)
This study presents an efficient transformer-based question-answering (QA) model optimized for deployment on a 13th Gen Intel i7-1355U CPU, using the Stanford Question Answering Dataset (SQuAD) v1.1. Leveraging exploratory data analysis, data augmentation, and fine-tuning of a DistilBERT architecture, the model achieves a validation F1 score of 0.6536 with an average inference time of 0.1208 seconds per question. Compared to a rule-based baseline (F1: 0.3124) and full BERT-based models, our approach offers a favorable trade-off between accuracy and computational efficiency. This makes it well-suited for real-time applications on resource-constrained systems. The study includes systematic evaluation of data augmentation strategies and hyperparameter configurations, providing practical insights into optimizing transformer models for CPU-based inference.
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