Edit model card

ScandiBERT

Note note: The model has been updated on 2022-09-27

The model was trained on the data shown in the table below. Batch size was 8.8k, the model was trained for 72 epochs on 24 V100 cards for about 2 weeks.

Language Data Size
Icelandic See IceBERT paper 16 GB
Danish Danish Gigaword Corpus (incl Twitter) 4,7 GB
Norwegian NCC corpus 42 GB
Swedish Swedish Gigaword Corpus 3,4 GB
Faroese FC3 + Sosialurinn + Bible 69 MB

Note: At an earlier date a half trained model went up here, it has since been removed. The model has since been updated.

This is a Scandinavian BERT model trained on a large collection of Danish, Faroese, Icelandic, Norwegian and Swedish text. It is currently the highest ranking model on the ScandEval leaderbord https://scandeval.github.io/pretrained/

If you find this model useful, please cite

@inproceedings{snaebjarnarson-etal-2023-transfer,
    title = "{T}ransfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese",
    author = "Snæbjarnarson, Vésteinn  and
      Simonsen, Annika  and
      Glavaš, Goran  and
      Vulić, Ivan",
    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
    month = "may 22--24",
    year = "2023",
    address = "Tórshavn, Faroe Islands",
    publisher = {Link{\"o}ping University Electronic Press, Sweden},
}
Downloads last month
11
Safetensors
Model size
125M params
Tensor type
I64
·
F32
·
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Datasets used to train vesteinn/ScandiBERT