To track trends in the perception of literary translation around the political transformation in 1989 in Hungary, a coding system was developed on the paragraphs of the 1980-1999 issues of the literary journal Alf\"old. This paper describes how we trained BERT models to carry over the coding system to the 1980-1999 issues of the literary journal Nagyvil\'ag. We use extensive hyperparameter tuning, loss functions robust to label unbalance, 10-fold cross-validation for precise evaluations and a model ensemble for prediction, manual validation on the predict set, a new calibration method to better predict label counts for sections of the Nagyvil\'ag corpus, and to study the relations between labels, we construct label relation networks.

翻译：为追踪1989年匈牙利政治转型前后文学翻译认知的趋势，本研究针对文学期刊《Alföld》1980-1999年各期段落开发了一套编码系统。本文阐述了如何训练BERT模型将该编码系统迁移至文学期刊《Nagyvilág》1980-1999年各期。我们采用了广泛的超参数调优、对标签不平衡具有鲁棒性的损失函数、用于精确评估的10折交叉验证、用于预测的模型集成、对预测集的人工验证、旨在更精准预测《Nagyvilág》语料库各章节标签计数的全新校准方法，并构建标签关系网络以研究标签间的关联。