论文标题
机器学习模型的比较研究和小队的BERT
Comparative Study of Machine Learning Models and BERT on SQuAD
论文作者
论文摘要
这项研究旨在对机器学习中流行的某些模型的性能以及Stanford问题回答数据集（Squead）的BERT模型进行比较分析。分析表明，与其他模型相比，BERT模型曾经是最先进的小队的准确性。但是，即使仅使用100个样本，BERT也需要更大的执行时间。这表明，随着精度的提高，更多的时间用于培训数据。尽管在初步机器学习模型的情况下，完整数据的执行时间较低，但准确性会损害。
This study aims to provide a comparative analysis of performance of certain models popular in machine learning and the BERT model on the Stanford Question Answering Dataset (SQuAD). The analysis shows that the BERT model, which was once state-of-the-art on SQuAD, gives higher accuracy in comparison to other models. However, BERT requires a greater execution time even when only 100 samples are used. This shows that with increasing accuracy more amount of time is invested in training the data. Whereas in case of preliminary machine learning models, execution time for full data is lower but accuracy is compromised.
