arXiv:2205.01335
공식 코드 · Papers with Code
Predicting Issue Types with seBERT
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
UniPTMs: The First Unified Multi-type PTM Site Prediction Model via Master-Slave Architecture-Based Multi-Stage Fusion Strategy and Hierarchical Contrastive Loss
Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data
GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning
CoCo-Agent: A Comprehensive Cognitive MLLM Agent for Smartphone GUI Automation
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
Learning Type Inference for Enhanced Dataflow Analysis
토론
이 논문과 연결된 포럼 글이 아직 없습니다.