IceBERT
IceBERT was trained with fairseq using the RoBERTa-base architecture. 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 |
If you find this model useful, please cite
@inproceedings{snaebjarnarson-etal-2022-warm,
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",
}
- Downloads last month
- 26
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.