Fill-Mask
Transformers
PyTorch
Safetensors
xlm-roberta
roberta
icelandic
norwegian
faroese
danish
swedish
masked-lm
Instructions to use vesteinn/ScandiBERT-no-faroese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vesteinn/ScandiBERT-no-faroese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vesteinn/ScandiBERT-no-faroese")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vesteinn/ScandiBERT-no-faroese") model = AutoModelForMaskedLM.from_pretrained("vesteinn/ScandiBERT-no-faroese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ScandiBERT-no-faroese
This is a version of the ScandiBERT model trained without any Faroese data and a different subword tokenizer.
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 |
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},
}
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