README.md

metadata

license: cc-by-nc-4.0
language:
  - de
  - fr
  - it
  - rm
  - gsw
  - multilingual
inference: false

SwissBERT is a masked language model for processing Switzerland-related text. It has been trained on more than 21 million Swiss news articles retrieved from Swissdox@LiRI.

SwissBERT is based on X-MOD, which has been pre-trained with language adapters in 81 languages. For SwissBERT we trained adapters for the national languages of Switzerland – German, French, Italian, and Romansh Grischun. In addition, we used a Switzerland-specific subword vocabulary.

The pre-training code and usage examples are available here. We also release a version that was fine-tuned on named entity recognition (NER): https://huggingface.co/ZurichNLP/swissbert-ner

Update 2024-01: Support for Swiss German

We added a Swiss German adapter to the model.

Languages

SwissBERT contains the following language adapters:

lang_id (Adapter index)	Language code	Language
0	de_CH	Swiss Standard German
1	fr_CH	French
2	it_CH	Italian
3	rm_CH	Romansh Grischun
4	gsw	Swiss German

License

Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

Usage (masked language modeling)

from transformers import pipeline

fill_mask = pipeline(model="ZurichNLP/swissbert")

German example

fill_mask.model.set_default_language("de_CH")
fill_mask("Der schönste Kanton der Schweiz ist <mask>.")

Output:

[{'score': 0.1373230218887329,
  'token': 331,
  'token_str': 'Zürich',
  'sequence': 'Der schönste Kanton der Schweiz ist Zürich.'},
 {'score': 0.08464793860912323,
  'token': 5903,
  'token_str': 'Appenzell',
  'sequence': 'Der schönste Kanton der Schweiz ist Appenzell.'},
 {'score': 0.08250337839126587,
  'token': 10800,
  'token_str': 'Graubünden',
  'sequence': 'Der schönste Kanton der Schweiz ist Graubünden.'},
 ...]

French example

fill_mask.model.set_default_language("fr_CH")
fill_mask("Je m'appelle <mask> Federer.")

Output:

[{'score': 0.9943694472312927,
  'token': 1371,
  'token_str': 'Roger',
  'sequence': "Je m'appelle Roger Federer."},
 ...]

Bias, Risks, and Limitations

• SwissBERT is mainly intended for tagging tokens in written text (e.g., named entity recognition, part-of-speech tagging), text classification, and the encoding of words, sentences or documents into fixed-size embeddings. SwissBERT is not designed for generating text.
• The model was adapted on written news articles and might perform worse on other domains or language varieties.
• While we have removed many author bylines, we did not anonymize the pre-training corpus. The model might have memorized information that has been described in the news but is no longer in the public interest.

Training Details

• Training data: German, French, Italian and Romansh documents in the Swissdox@LiRI database, until 2022.
• Training procedure: Masked language modeling

The Swiss German adapter was trained on the following two datasets of written Swiss German:

1. SwissCrawl (Linder et al., LREC 2020), a collection of Swiss German web text (forum discussions, social media).
2. A custom dataset of Swiss German tweets

Environmental Impact

• Hardware type: RTX 2080 Ti.
• Hours used: 10 epochs × 18 hours × 8 devices = 1440 hours
• Site: Zurich, Switzerland.
• Energy source: 100% hydropower (source)
• Carbon efficiency: 0.0016 kg CO2e/kWh (source)
• Carbon emitted: 0.6 kg CO2e (source)

Citations

@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",
}

Swiss German adapter:

@inproceedings{vamvas-etal-2024-modular,
      title={Modular Adaptation of Multilingual Encoders to Written Swiss German Dialect},
      author={Jannis Vamvas and No{\"e}mi Aepli and Rico Sennrich},
      booktitle={First Workshop on Modular and Open Multilingual NLP},
      year={2024},
}