Instructions to use MLRS/mBERTu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLRS/mBERTu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="MLRS/mBERTu")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("MLRS/mBERTu") model = AutoModelForMaskedLM.from_pretrained("MLRS/mBERTu") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c52342b671b9779b9871bad13c0e41cfb1501433f2e8d0c1b8c21620ccb7df5
- Size of remote file:
- 712 MB
- SHA256:
- 8baecbaa54ce539711b8d82f6637dec8de9b5addaf0b8919399c0207825a896d
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