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.

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