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
Download pytorch_model.bin from BSC-LT/MrBERT-ca: direct link, hf CLI and curl.
- Browser
- Download file 598 MB
-
https://huggingface.co/BSC-LT/MrBERT-ca/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BSC-LT/MrBERT-ca/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BSC-LT/MrBERT-ca/resolve/main/pytorch_model.bin
598 MB
- Xet hash:
- aaf0077f4088b62b8a2a0434dbb6a0d9e4d18bcf419713832be810d607ff27bb
- Size of remote file:
- 598 MB
- SHA256:
- ba479d0547002c22a39a134463471a782952e4f964703df58cd27b7b27d33bd9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.