# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("formulae/reBERT-multilingual")
model = AutoModelForMaskedLM.from_pretrained("formulae/reBERT-multilingual", device_map="auto")Quick Links
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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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="formulae/reBERT-multilingual")