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fransis3/
EuroBERT-610m-NorNER

Name: EuroBERT-610m-NorNER
Availability: InStock

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EuroBERT-610m-NorNER

A Norwegian named entity recognition model fine-tuned from EuroBERT/EuroBERT-610m on the NorNE dataset, covering both Bokmål and Nynorsk.

Model Details

Author: Fransis Nyka Kolstø
Base model: EuroBERT/EuroBERT-610m
Language(s): Norwegian Bokmål (nb), Norwegian Nynorsk (nn)
Task: Token classification / Named Entity Recognition
Tagging scheme: IOB2
License: Apache 2.0

Entity Types

The model predicts 9 entity types using the IOB2 scheme described in the NbAiLab norne dataset

Intended Use

The model is intended for named entity recognition on Norwegian text (Bokmål and Nynorsk), including news, blog posts, parliamentary proceedings, and government reports — reflecting the genre distribution of the NorNE data.

Training Procedure

Training was done in two phases on the NorNE dataset:

Phase 1 — Optimal-step search: The model was trained on the train split with the dev split used for evaluation and early stopping. Training proceeded through a curriculum of increasing input context lengths, allowing the model to adapt progressively from sentence-level to longer multi-sentence contexts.
Phase 2 — Final training: The base model was re-initialized and trained on the combined train + development splits, replaying the same curriculum and learning-rate trajectory as Phase 1, but stopping each stage at the best steps identified in phase 1. This allows the final model to benefit from the additional development data without re-tuning.

Evaluation

Evaluated on the NorNE test split (Bokmål and Nynorsk combined), with entity-level metrics computed via seqeval:

Metric	Score
Precision	0.8209
Recall	0.8257
F1	0.8233

Usage

python

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "fransis3/EuroBERT-610m-NorNER"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id, trust_remote_code=True)

ner = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
ner("Erna Solberg besøkte Universitetet i Oslo forrige uke.")

Limitations

Performance is reported on NorNE's test distribution (news, blogs, parliamentary text, government reports). Generalization to other domains (e.g., social media, clinical text, historical Norwegian) is not guaranteed.
The model inherits any biases present in its pretraining data (EuroBERT) and in NorNE's source texts.
The base model is loaded with trust_remote_code=True as required by EuroBERT.

Dataset

NorNE is a named entity annotation layer over the Norwegian Dependency Treebank, covering both Bokmål and Nynorsk.

License

This model is released under the Apache 2.0 license, matching the base model. The NorNE annotations used for training are released under CC0 1.0.

Citation

If you use this model, please cite the underlying resources:

EuroBERT:

bibtex

@misc{boizard2025eurobertscalingmultilingualencoders,
      title={EuroBERT: Scaling Multilingual Encoders for European Languages}, 
      author={Nicolas Boizard and Hippolyte Gisserot-Boukhlef and Duarte M. Alves and André Martins and Ayoub Hammal and Caio Corro and Céline Hudelot and Emmanuel Malherbe and Etienne Malaboeuf and Fanny Jourdan and Gabriel Hautreux and João Alves and Kevin El-Haddad and Manuel Faysse and Maxime Peyrard and Nuno M. Guerreiro and Patrick Fernandes and Ricardo Rei and Pierre Colombo},
      year={2025},
      eprint={2503.05500},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.05500}, 
}

NorNE:

bibtex

@misc{jørgensen2020norneannotatingnamedentities,
      title={NorNE: Annotating Named Entities for Norwegian}, 
      author={Fredrik Jørgensen and Tobias Aasmoe and Anne-Stine Ruud Husevåg and Lilja Øvrelid and Erik Velldal},
      year={2020},
      eprint={1911.12146},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/1911.12146}, 
}