Model type: Transformer-based masked language model
Training data: No additional pretraining, merges two existing models
Languages: 100+ languages
Architecture:
- Base architectures:
- XLM-RoBERTa base (multilingual)
- BERT base cased (multilingual)
Custom merging technique to combine weights from both base models into one unified model
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