论文标题
与Sebert预测问题类型
Predicting Issue Types with seBERT
论文作者
论文摘要
预训练的变压器模型是自然语言模型处理的当前最新技术。 Sebert是一种基于Bert体系结构开发的模型，但使用软件工程数据进行了培训。我们为问题类型预测的任务进行了NLBSE挑战，对此模型进行了微调。我们的模型在召回率和Precisio中的所有三种问题类型的基线FastText中都占据主导地位，以达到85.7％的总体F1评分，这比基线增长了4.1％。
Pre-trained transformer models are the current state-of-the-art for natural language models processing. seBERT is such a model, that was developed based on the BERT architecture, but trained from scratch with software engineering data. We fine-tuned this model for the NLBSE challenge for the task of issue type prediction. Our model dominates the baseline fastText for all three issue types in both recall and precisio} to achieve an overall F1-score of 85.7%, which is an increase of 4.1% over the baseline.
