pytorch roberta fill-mask is arxiv:2201.05601 transformers icelandic masked-lm license:agpl-3.0 autotrain_compatible
---
language: is
widget:
- text: Má bjóða þér <mask> í kvöld?
- text: Forseti <mask> er ágæt.
- text: Súpan var <mask> á bragðið.
tags:
- roberta
- icelandic
- masked-lm
- pytorch
license: agpl-3.0
---

# IceBERT

This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below.

| Dataset                                              | Size    | Tokens |
|------------------------------------------------------|---------|--------|
| Icelandic Gigaword Corpus v20.05 (IGC)               | 8.2 GB  | 1,388M |
| Icelandic Common Crawl Corpus (IC3)                  | 4.9 GB  | 824M   |
| Greynir News articles                                | 456 MB  | 76M    |
| Icelandic Sagas                                      | 9 MB    | 1.7M   |
| Open Icelandic e-books (Rafbókavefurinn)             | 14 MB   | 2.6M   |
| Data from the medical library of Landspitali         | 33 MB   | 5.2M   |
| Student theses from Icelandic universities (Skemman) | 2.2 GB  | 367M   |
| Total                                                | 15.8 GB | 2,664M |

## Citation

The model is described in this paper [https://arxiv.org/abs/2201.05601](https://arxiv.org/abs/2201.05601). Please cite the paper if you make use of the model.

```
@article{DBLP:journals/corr/abs-2201-05601,
  author    = {V{\'{e}}steinn Sn{\ae}bjarnarson and
               Haukur Barri S{\'{\i}}monarson and
               P{\'{e}}tur Orri Ragnarsson and
               Svanhv{\'{\i}}t Lilja Ing{\'{o}}lfsd{\'{o}}ttir and
               Haukur P{\'{a}}ll J{\'{o}}nsson and
               Vilhj{\'{a}}lmur {\TH}orsteinsson and
               Hafsteinn Einarsson},
  title     = {A Warm Start and a Clean Crawled Corpus - {A} Recipe for Good Language
               Models},
  journal   = {CoRR},
  volume    = {abs/2201.05601},
  year      = {2022},
  url       = {https://arxiv.org/abs/2201.05601},
  eprinttype = {arXiv},
  eprint    = {2201.05601},
  timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2201-05601.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

