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NorBERT: Bidirectional Encoder Representations from Transformers

Training corpus

We use clean training corpora with ordered sentences:

In total, this comprises about two billion word tokens, both in Bøkmal and in Nynorsk; thus, this is a joint model. In the future, separate Børmal and Nynorsk models are planned as well.


1. Wikipedia texts were extracted using segment_wiki.

2. In NAK, for years up to 2005, the text is in the one-token-per-line format. There are special delimiters signaling the beginning of a new document and providing the URLs. We converted this to running text using a self-made de-tokenizer.

3. In NAK, everything up to and including 2011 is in the ISO 8859-01 encoding ('Latin-1'). These files were converted to UTF-8 before any other pre-processing.

4. The resulting corpus was sentence-segmented using Stanza. We left blank lines between documents (and sections in the case of Wikipedia) so that the "next sentence prediction" task doesn't span between documents.


The vocabulary for the model is of size 30 000 and contains cased entries with diacritics. It is generated from raw text, without, e.g., separating punctuation from word tokens. This means one can feed raw text into NorBERT.

The vocabulary was generated using the SentencePiece algorithm and Tokenizers library (code). The resulting Tokenizers model was converted to the standard BERT WordPiece format.

NorBERT model


NorBERT corresponds in its configuration to Google's Bert-Base Cased for English, with 12 layers and hidden size 768. Configuration file

Training overview

NorBERT was trained on the Norwegian academic HPC system called Saga. Most of the time the training was distributed across 4 compute nodes and 16 NVIDIA P100 GPUs. Training took approximately 3 weeks. Instructions for reproducing the training setup with EasyBuild

Training code

Similar to the creators of FinBERT, we employed the implementation by NVIDIA which allows relatively fast multi-node and multi-GPU training.