XLM-RoBERTa (XLM-R) builds upon the robustly optimized BERT pretraining approach () by eliminating the next-sentence prediction objective and training on massive, multilingual CommonCrawl web corpora. It uses a shared vocabulary across more than 100 languages, establishing a latent embedding space where semantically similar concepts align across different scripts and syntaxes. WALS Dataset (The Typology Blueprint)
Ensure that Python (3.9 or newer) and pip are installed on your system. wals roberta sets upd
For languages not well-represented by an English-centric model like roberta-base , you can use XLM-RoBERTa . This model is pretrained on text from 100 different languages, making it much more suitable for working with the diverse set of languages found in WALS. The setup code is almost identical; you would just replace model_name = "roberta-base" with model_name = "xlm-roberta-base" . XLM-RoBERTa (XLM-R) builds upon the robustly optimized BERT