OldBERTur: Named Entity Recognition for Normalised Old Icelandic
This model performs Named Entity Recognition (NER) on normalised Old Icelandic texts sourced from medieval manuscripts, identifying Person and Location entities.
Please note, while the model is fully functional, this model card is due to be updated in the near future with supplementary information.
This model is fine-tuned from IceBERT for NER, designed for normalised Old Icelandic texts as defined in the Menota normalised transcription level description.
Named entity recognition in normalised Old Icelandic texts
Digital humanities research on Medieval Icelandic literature
Semi-automatic annotation of historical Icelandic documents
Information extraction from saga literature and historical texts
How to Use
python
1from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
23# Load model and tokenizer4tokenizer = AutoTokenizer.from_pretrained("Riksarkivet/oldbertur-normalised-old-icelandic-ner")5model = AutoModelForTokenClassification.from_pretrained("Riksarkivet/oldbertur-normalised-old-icelandic-ner")67# Use aggregation_strategy="first" to properly combine subword tokens8ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="first")910text ="Í þann tíma var hǫfðingi ágǽtr á Íslandi í Ísafirði, er Vermundr hét"11results = ner_pipeline(text)1213for entity in results:14print(f"{entity['word']}: {entity['entity_group']} ({entity['score']:.3f})")
Expected output:
Íslandi: Location (1.000)
Ísafirði,: Location (1.000)
Vermundr: Person (1.000)
Training Data
This model uses the (M + I)R + MIM training configuration, combining:
Source
Description
Menota (M)
Normalised Old Icelandic texts from the Medieval Nordic Text Archive
IcePaHC (I)
Icelandic Parsed Historical Corpus (normalised Old Icelandic texts)
MIM-GOLD-NER (MIM)
Modern Icelandic NER data for data augmentation
The superscript R means that sentence-level class resampling (sCR) was applied to both Menota and IcePaHC data. Since both sources use normalised orthography, both are resampled to address entity class imbalance.
The dev and test sets consist exclusively of Old Icelandic texts in order to reflect our target domain.
Evaluation Results
Metric
Score
F1
0.93
Precision
0.90
Recall
0.95
Labels
The model uses BIO tagging with the following labels:
Label
Description
O
Outside any entity
B-Person
Beginning of a person name
I-Person
Inside/continuation of a person name
B-Location
Beginning of a location name
I-Location
Inside/continuation of a location name
Limitations
Orthography: This model is trained on normalised texts. For diplomatic transcriptions, use the diplomatic variation of this model.
Entity types: Only Person and Location entities are supported. Other entity types (organisations, dates, etc.) are not recognised due to scarcity in the training data.
Time period: Primarily trained on texts from 1250-1400 CE. Performance may vary on texts from other periods.
Domain: Optimised for saga literature and historical texts. May perform differently on other text types.
Training Procedure
NER is framed as a token classification task, with a classification head added on top of IceBERT.
Hyperparameters:
Base model: mideind/IceBERT
Epochs: 5
Learning rate: 2e-5
Batch size: 16
Max sequence length: 256 tokens
Warm-up ratio: 10%
Weight decay: 0.01
Class imbalance handling:
Weighted cross-entropy loss with class weights: 0.1 for non-entities (O), 30.0 for entity classes to address class imbalance in the training data.
Citation
If you use this model, please cite:
bibtex
1@inproceedings{henningsson2026,
2 title={{OldBERTur}: Named Entity Recognition for Medieval {I}celandic},
3 author={Henningsson, Pontus and Pettersson, Eva and Lenas, Erik},
4 booktitle={{Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-2026}},
5 year={2026},
6 address = {Palma, Mallorca, Spain},
7 pages = {469–481. European Language Resources Association (ELRA)},
8 publisher = "11 May 2026. European Language Resources Association (ELRA)",
9 url = {http://lrec-conf.org/proceedings/lrec2026/workshops/lt4hala/2026.lt4hala-1.0.pdf}
10}
Resources
For more information on our code and data, see our GitHub repository. For more information about the work in general, see our paper.
We are grateful for the great work carried out by the projects below, and for making it possible for us to use their data in order to conduct our academic research and develop NER models for Medieval Icelandic. We thank developers, annotators, scholars, project managers, and anyone else who has contributed to these projects. We also express our sincerest gratitude to the students from Uppsala University who assisted in marking and annotating entities in the two Menota works Codex Wormianus (AM 242 fol) and Vǫluspá in Hauksbók (AM 544 4to).
