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LitLat BERT is a trilingual model, using xlm-roberta-base architecture, trained on Lithuanian, Latvian, and English corpora. Focusing on three languages, the model performs better than multilingual BERT, while still offering an option for cross-lingual knowledge transfer, which a monolingual model wouldn't.

Named entity recognition evaluation

We compare LitLat BERT with multilingual BERT (mBERT), XLM-RoBERTa (XLM-R) and monolingual Latvian BERT (LVBERT) (Znotins and Barzdins, 2020). The report the results as a macro F1 score of 3 named entity classes shared in all three datasets: person, location, organization.

Language mBERT XLM-R LVBERT LitLat
Latvian 0.830 0.865 0.797 0.881
Lithuanian 0.797 0.817 / 0.850
English 0.939 0.937 / 0.943
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