Continued Pretraining of FinBERT on Finnish Histopathological Reports: Train-Time Signals and Proxy Downstream Correlations

arXiv:2604.14815 · cs.CL · Submitted 2026-04-16 · Read on arXiv

cs.CL

Submitted: 2026-04-16

Updated: 2026-09-24

Code: https://github.com/aajanki/eduskunta-vkk

Terminology

• Fine-Tuning — 21× in this paper · explained in 5 episodes
• Clustering — 12× in this paper · explained in 2 episodes
• Embeddings — 10× in this paper · explained in 2 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Adaptation — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Point Clouds — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Low-Resource Languages — 3× in this paper · explained in 4 episodes
• Scaling Laws — 3× in this paper · explained in 4 episodes
• Centered Kernel Alignment — 2× in this paper
• Centered Kernel Alignment (CKA) — 2× in this paper · explained in 2 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Transformer Models — 2× in this paper · explained in 2 episodes
• Adjusted Rand Index — 1× in this paper
• Error Reduction Rate — 1× in this paper
• F1 Score — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Normalized Mutual Information — 1× in this paper
• Perplexity — 1× in this paper · explained in 3 episodes
• Representational Similarity Analysis — 1× in this paper
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Universal Language Model Fine-tuning for Text Classification
• Attention Is All You Need
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
• Scaling Laws for Neural Language Models
• Training Compute-Optimal Large Language Models
• Loss-to-Loss Prediction: Scaling Laws for All Datasets
• Multilingual is not enough: BERT for Finnish
• Classifying the classifier: dissecting the weight space of neural networks
• Measuring the Measures: Discriminative Capacity of Representational Similarity Metrics Across Model Families
• Evaluating Representational Similarity Measures from the Lens of Functional Correspondence
• Offline bilingual word vectors, orthogonal transformations and the inverted softmax
• All-but-the-Top: Simple and Effective Postprocessing for Word Representations

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