This model is a derivative of
Maltehb/danish-bert-botxo adapted to legal text. It has been pre-trained on a combination of the Danish part of the MultiEURLEX (Chalkidis et al., 2021) dataset comprising EU legislation and two subsets (
retsinformationdk,
retspraksis) of the Danish Gigaword Corpus (Derczynski et al., 2021) comprising legal proceedings.
It achieves the following results on the evaluation set:
This is a BERT model (Devlin et al., 2018) model pre-trained on Danish legal corpora. It follows a base configuration with 12 Transformer layers, each one with 768 hidden units and 12 attention heads.
This model is pre-training on a combination of the Danish part of the MultiEURLEX dataset and two subsets (retsinformationdk, retspraksis) of the Danish Gigaword Corpus.
The model was initially pre-trained for 500k steps with sequences up to 128 tokens, and then continued pre-training for additional 100k with sequences up to 512 tokens.
