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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


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

    title = "A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models",
    author = "Sn{\ae}bjarnarson, V{\'e}steinn  and
      S{\'\i}monarson, Haukur Barri  and
      Ragnarsson, P{\'e}tur Orri  and
      Ing{\'o}lfsd{\'o}ttir, Svanhv{\'\i}t Lilja  and
      J{\'o}nsson, Haukur  and
      Thorsteinsson, Vilhjalmur  and
      Einarsson, Hafsteinn",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.lrec-1.464",
    pages = "4356--4366",
    abstract = "We train several language models for Icelandic, including IceBERT, that achieve state-of-the-art performance in a variety of downstream tasks, including part-of-speech tagging, named entity recognition, grammatical error detection and constituency parsing. To train the models we introduce a new corpus of Icelandic text, the Icelandic Common Crawl Corpus (IC3), a collection of high quality texts found online by targeting the Icelandic top-level-domain .is. Several other public data sources are also collected for a total of 16GB of Icelandic text. To enhance the evaluation of model performance and to raise the bar in baselines for Icelandic, we manually translate and adapt the WinoGrande commonsense reasoning dataset. Through these efforts we demonstrate that a properly cleaned crawled corpus is sufficient to achieve state-of-the-art results in NLP applications for low to medium resource languages, by comparison with models trained on a curated corpus. We further show that initializing models using existing multilingual models can lead to state-of-the-art results for some downstream tasks.",
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