Instructions to use Cube/ShijiBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cube/ShijiBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Cube/ShijiBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Cube/ShijiBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_bert.bin from Cube/ShijiBERT: direct link, hf CLI and curl.
- Browser
- Download file 412 MB
-
https://huggingface.co/Cube/ShijiBERT/resolve/main/pytorch_bert.bin
- Command line
-
hf download hf://Cube/ShijiBERT/pytorch_bert.bin
-
curl -L -o pytorch_bert.bin https://huggingface.co/Cube/ShijiBERT/resolve/main/pytorch_bert.bin
412 MB
- Xet hash:
- a4e3c397c3d3cebf2b29526c9668bebc08a51e516bdaa21527104155be822795
- Size of remote file:
- 412 MB
- SHA256:
- 0784da6ee49265b6460e1df5598832aacd9057833bbd9eae463b08a77d16c2bc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.