English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work, we show the process of building a large-scale training set fromdigital and digitized collections at a national library. The resultingBidirectional Encoder Representations from Transformers (BERT)-based languagemodel for Norwegian outperforms multilingual BERT (mBERT) models in severaltoken and sequence classification tasks for both Norwegian Bokm{\aa}l andNorwegian Nynorsk. Our model also improves the mBERT performance for otherlanguages present in the corpus such as English, Swedish, and Danish. Forlanguages not included in the corpus, the weights degrade moderately whilekeeping strong multilingual properties. Therefore, we show that buildinghigh-quality models within a memory institution using somewhat noisy opticalcharacter recognition (OCR) content is feasible, and we hope to pave the wayfor other memory institutions to follow.

Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model