Update README.md

README.md

@@ -1,3 +1,160 @@
----
-license: apache-2.0
----
+---
+language:
+- 'no'
+- nb
+- nn
+license: apache-2.0
+library_name: transformers
+tags:
+- token-classification
+- named-entity-recognition
+- ner
+- norwegian
+- bokmal
+- nynorsk
+- norbert
+base_model: ltg/norbert4-xlarge
+datasets:
+- NbAiLab/norne
+metrics:
+- precision
+- recall
+- f1
+pipeline_tag: token-classification
+model-index:
+- name: norbert4-xlarge-NorNER
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: NorNE
+      type: ltg/norne
+      split: test
+    metrics:
+    - type: precision
+      value: 0.9069
+    - type: recall
+      value: 0.9164
+    - type: f1
+      value: 0.9116
+      name: Entity-level F1 (seqeval)
+---
+
+# norbert4-xlarge-NorNER
+
+A Norwegian named entity recognition model fine-tuned from [ltg/norbert4-xlarge](https://huggingface.co/ltg/norbert4-xlarge) on the [NorNE](https://huggingface.co/datasets/NbAiLab/norne) dataset, covering both Bokmål and Nynorsk.
+
+## Model Details
+
+- **Author:** Fransis Nyka Kolstø
+- **Base model:** [ltg/norbert4-xlarge](https://huggingface.co/ltg/norbert4-xlarge)
+- **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](https://huggingface.co/datasets/NbAiLab/norne#annotations)
+
+## 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:
+
+1. **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.
+
+2. **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 | 0.9069 |
+| Recall | 0.9164 |
+| **F1** | **0.9116** |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+
+model_id = "fransis3/norbert4-xlarge-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](https://huggingface.co/datasets/NbAiLab/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:
+
+```bibtex
+@inproceedings{charpentier-samuel-2024-bert,
+    title = "{GPT} or {BERT}: why not both?",
+    author = "Charpentier, Lucas Georges Gabriel  and
+      Samuel, David",
+    booktitle = "The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning",
+    month = nov,
+    year = "2024",
+    address = "Miami, FL, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.conll-babylm.24/",
+    pages = "262--283"
+}
+```
+
+```bibtex
+@inproceedings{samuel-etal-2023-norbench,
+    title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models",
+    author = "Samuel, David  and
+      Kutuzov, Andrey  and
+      Touileb, Samia  and
+      Velldal, Erik  and
+      {\O}vrelid, Lilja  and
+      R{\o}nningstad, Egil  and
+      Sigdel, Elina  and
+      Palatkina, Anna",
+    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
+    month = may,
+    year = "2023",
+    address = "T{\'o}rshavn, Faroe Islands",
+    publisher = "University of Tartu Library",
+    url = "https://aclanthology.org/2023.nodalida-1.61",
+    pages = "618--633"
+}
+```
+
+```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}, 
+}
+```