metadata

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
French: rts.ch
Italian: rsi.ch
Romansh Grischun: 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.

License

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

@article{vamvas-etal-2023-swissbert,
      title={Swiss{BERT}: The Multilingual Language Model for Switzerland}, 
      author={Jannis Vamvas and Johannes Gra\"en and Rico Sennrich},
      year={2023},
      eprint={2303.13310},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2303.13310}
}