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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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