Instructions to use BSC-LT/MrBERT-ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/MrBERT-ca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BSC-LT/MrBERT-ca")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BSC-LT/MrBERT-ca") model = AutoModelForMaskedLM.from_pretrained("BSC-LT/MrBERT-ca") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1ca65a8366674f0f711c9f9811c9b0904be1761a49da070b4e53055e42db1b3
- Size of remote file:
- 1.06 MB
- SHA256:
- 97523bb11033b57d9a7064690991ac731e1647e4370f1d5273720e7772d4f447
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