README.md

---
language:
  - da
  - no
license: cc-by-4.0
datasets:
  - MiMe-MeMo/Corpus-v1.1
  - MiMe-MeMo/Sentiment-v1
  - MiMe-MeMo/WSD-Skaebne
metrics:
  - f1
tags:
  - historical-texts
  - digital-humanities
  - sentiment-analysis
  - word-sense-disambiguation
  - danish
  - norwegian
model-index:
  - name: MeMo-BERT-01
    results:
      - task:
          type: text-classification
          name: Sentiment Analysis
        dataset:
          name: MiMe-MeMo/Sentiment-v1
          type: text
        metrics:
          - name: f1
            type: f1
            value: 0.56
      - task:
          type: text-classification
          name: Word Sense Disambiguation
        dataset:
          name: MiMe-MeMo/WSD-Skaebne
          type: text
        metrics:
          - name: f1
            type: f1
            value: 0.43
---

# MeMo-BERT-01

**MeMo-BERT-01** is a pre-trained language model for **historical Danish and Norwegian literary texts** (1870–1900).  
It was introduced in [Al-Laith et al. (2024)](https://aclanthology.org/2024.lrec-main.431/) as part of the first dedicated PLMs for historical Danish and Norwegian.

## Model Description

- **Architecture:** BERT-base (12 layers, hidden size 768, 12 attention heads, vocab size 30k)  
- **Pre-training strategy:** Trained **from scratch** on the MeMo corpus (no prior pre-training)  
- **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:** ~44 hours  

This model represents the **baseline historical-domain model** trained entirely on 19th-century Scandinavian novels.

## 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.  
  - High-stakes applications (e.g., legal, medical, political decision-making).  

## Training Data

- **Corpus:** [MeMo Corpus v1.1](https://huggingface.co/datasets/MiMe-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 | **0.56** | 3-class (pos/neg/neu) |
| Word Sense Disambiguation | MiMe-MeMo/WSD-Skaebne | **0.43** | 4-class (pre-modern, modern, figure of speech, ambiguous) |

### Comparison

MeMo-BERT-01 performs **worse than MeMo-BERT-03** (continued pre-training), highlighting the limitations of training from scratch on historical data without leveraging contemporary PLMs.

## Limitations

- Trained **only from scratch** on ~53M words (relatively small for BERT training).  
- Underperforms compared to continued pre-training (MeMo-BERT-03).  
- Domain-specific to late 19th-century novels.  
- OCR and normalization errors may remain in training corpus.  

## Ethical Considerations

- All texts are **public domain** (authors deceased).  
- Datasets released under **CC BY 4.0**.  
- No sensitive personal data involved.  

## Citation

If you use this model, please cite:

```bibtex
@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/"
}