---
dataset_info:
  features:
  - name: tokens
    sequence: string
  - name: ner_tags
    sequence: string
  - name: url
    dtype: string
  splits:
  - name: test_de
    num_bytes: 164433
    num_examples: 200
  - name: test_fr
    num_bytes: 186036
    num_examples: 200
  - name: test_it
    num_bytes: 197513
    num_examples: 200
  - name: test_rm
    num_bytes: 206644
    num_examples: 200
  download_size: 220352
  dataset_size: 754626
license: cc-by-4.0
task_categories:
- token-classification
task_ids:
  - named-entity-recognition
language:
- de
- fr
- it
- rm
multilinguality:
  - multilingual
pretty_name: SwissNER
size_categories:
- n<1K
---

# SwissNER

A multilingual test set for named entity recognition (NER) on Swiss news articles.

## Description

SwissNER is a dataset for named entity recognition based on manually annotated news articles in Swiss Standard German, French, Italian, and Romansh Grischun.
We have manually annotated a selection of articles that have been published in February 2023 in the categories "Switzerland" or "Regional" on the following online news portals:
- Swiss Standard German: [srf.ch](https://www.srf.ch/)
- French: [rts.ch](https://www.rts.ch/)
- Italian: [rsi.ch](https://www.rsi.ch/)
- Romansh Grischun: [rtr.ch](https://www.rtr.ch/)

For each article we extracted the first two paragraphs after the lead paragraph.
We followed the guidelines of the CoNLL-2002 and 2003 shared tasks and annotated the names of persons, organizations, locations and miscellaneous entities.
The annotation was performed by a single annotator.

When using this dataset, please consider citing our paper, ["SwissBERT: The Multilingual Language Model for Switzerland"](https://aclanthology.org/2023.swisstext-1.6/) (SwissText 2023).

## License
- Text paragraphs: © Swiss Broadcasting Corporation (SRG SSR)
- Annotations: Attribution 4.0 International (CC BY 4.0)

## Statistics
|                      |   DE |    FR |    IT |    RM | Total |
|----------------------|-----:|------:|------:|------:|------:|
| Number of paragraphs |  200 |   200 |   200 |   200 |   800 |
| Number of tokens     | 9498 | 11434 | 12423 | 13356 | 46711 |
| Number of entities   |  479 |   475 |   556 |   591 |  2101 |
| – `PER`              |  104 |    92 |    93 |   118 |   407 |
| – `ORG`              |  193 |   216 |   266 |   227 |   902 |
| – `LOC`              |  182 |   167 |   197 |   246 |   792 |
| – `MISC`             |  113 |    79 |    88 |    39 |   319 |

## Citation
```bibtex
@inproceedings{vamvas-etal-2023-swissbert,
    title = "{S}wiss{BERT}: The Multilingual Language Model for {S}witzerland",
    author = {Vamvas, Jannis  and
      Gra{\"e}n, Johannes  and
      Sennrich, Rico},
    editor = {Ghorbel, Hatem  and
      Sokhn, Maria  and
      Cieliebak, Mark  and
      H{\"u}rlimann, Manuela  and
      de Salis, Emmanuel  and
      Guerne, Jonathan},
    booktitle = "Proceedings of the 8th edition of the Swiss Text Analytics Conference",
    month = jun,
    year = "2023",
    address = "Neuchatel, Switzerland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.swisstext-1.6",
    pages = "54--69",
}
```