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Domain Fine-Tuning FinBERT on Finnish Histopathological Reports: Train-Time Signals and Downstream Correlations

2026-04-16 09:36:48
Rami Luisto, Liisa Pet\"ainen, Tommi Gr\"onholm, Jan B\"ohm, Maarit Ahtiainen, Tomi Lilja, Ilkka P\"ol\"onen, Sami \"Ayr\"am\"o
arXiv_CL
             
Abstract

In NLP classification tasks where little labeled data exists, domain fine-tuning of transformer models on unlabeled data is an established approach. In this paper we have two aims. (1) We describe our observations from fine-tuning the Finnish BERT model on Finnish medical text data. (2) We report on our attempts to predict the benefit of domain-specific pre-training of Finnish BERT from observing the geometry of embedding changes due to domain fine-tuning. Our driving motivation is the common\situation in healthcare AI where we might experience long delays in acquiring datasets, especially with respect to labels.

Abstract (translated)

URL

https://arxiv.org/abs/2604.14815

PDF

https://arxiv.org/pdf/2604.14815.pdf

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