MeMo-BERT-03

MeMo-BERT-03 is a pre-trained language model for historical Danish and Norwegian literary texts (1870–1900).
It was introduced in Al-Laith et al. (2024) as part of the first dedicated PLMs for historical Danish and Norwegian.

Model Description

• Architecture: XLM-RoBERTa-base (24 layers, 1024 hidden size, 16 heads, vocab size 250k)
• Pre-training strategy: Continued pre-training of DanskBERT on historical data
• Training objective: Masked Language Modeling (MLM, 15% masking)
• Training data: MeMo Corpus v1.1 (839 novels, ~53M words, 1870–1900)
• Hardware: 2 × A100 GPUs
• Training time: ~32 hours

Intended Use

• Primary tasks:

  • Sentiment Analysis (positive, neutral, negative)
  • Word Sense Disambiguation (historical vs. modern senses of skæbne, "fate")

• Intended users:

  • Researchers in Digital Humanities, Computational Linguistics, and Scandinavian Studies.
  • Historians of literature studying 19th-century Scandinavian novels.

• Not intended for:

  • Contemporary Danish/Norwegian NLP tasks (performance may degrade).
  • High-stakes applications (e.g., legal, medical, political decision-making).

Training Data

• Corpus: MeMo Corpus v1.1 (Bjerring-Hansen et al. 2022)
• Time period: 1870–1900
• Size: 839 novels, 690 MB, 3.2M sentences, 52.7M words
• Preprocessing: OCR-corrected, normalized to modern Danish spelling, tokenized, lemmatized, annotated

Evaluation

Benchmarks

Task	Dataset	Test F1	Notes
Sentiment Analysis	MiMe-MeMo/Sentiment-v1	0.77	3-class (pos/neg/neu)
Word Sense Disambiguation	MiMe-MeMo/WSD-Skaebne	0.61	4-class (pre-modern, modern, figure of speech, ambiguous)

Comparison

MeMo-BERT-03 outperforms MeMo-BERT-1, MeMo-BERT-2, and contemporary baselines (DanskBERT, ScandiBERT, DanBERT, BotXO) across both tasks.

Limitations

• Domain-specific: trained only on novels from 1870–1900.
• May not generalize to other genres (newspapers, folk tales, poetry).
• Evaluation datasets are relatively small.
• OCR/normalization errors remain in some texts.

Ethical Considerations

• All texts are public domain (authors deceased).
• Datasets released under CC BY 4.0.
• Word sense annotations created by literary scholars, no sensitive personal data.

Citation

If you use this model, please cite:

@inproceedings{al-laith-etal-2024-development,
    title = "Development and Evaluation of Pre-trained Language Models for Historical {D}anish and {N}orwegian Literary Texts",
    author = "Al-Laith, Ali and Conroy, Alexander and Bjerring-Hansen, Jens and Hershcovich, Daniel",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    pages = "4811--4819",
    url = "https://aclanthology.org/2024.lrec-main.431/"
}