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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26cff623148c93c09a72f066638e3f924cb7fe60264d05abdbf9963532816d01
- Size of remote file:
- 3.53 MB
- SHA256:
- 8b3d416fe92096abf75f6c6bce8e68745d7c288dc88c0f7a63f0ec2b14c1b9a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.