Instructions to use KISTI-AI/scideberta-cs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KISTI-AI/scideberta-cs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="KISTI-AI/scideberta-cs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("KISTI-AI/scideberta-cs") model = AutoModelForMaskedLM.from_pretrained("KISTI-AI/scideberta-cs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from KISTI-AI/scideberta-cs: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/KISTI-AI/scideberta-cs/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://KISTI-AI/scideberta-cs/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/KISTI-AI/scideberta-cs/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- db611dbdc3a180892afb51366ac6fb01925b444d5d267b6b56c08ed0526b3317
- Size of remote file:
- 557 MB
- SHA256:
- 7bb4e6f8fbd18484bd29ad29ed835829d7526ce015478ad612c2cfe58ecfe540
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.