Instructions to use readerbench/jurBERT-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use readerbench/jurBERT-base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("readerbench/jurBERT-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed945b702e605dfd247bab90caf87d26569411c21554e2625940908789342883
- Size of remote file:
- 447 MB
- SHA256:
- 7c4f1b75616ef27cb21f65500cb282b84c41f9de2acd04c3bad695dfdc051011
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.