Instructions to use KBLab/bert-base-swedish-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/bert-base-swedish-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="KBLab/bert-base-swedish-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("KBLab/bert-base-swedish-cased") model = AutoModelForMaskedLM.from_pretrained("KBLab/bert-base-swedish-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from KBLab/bert-base-swedish-cased: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/KBLab/bert-base-swedish-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://KBLab/bert-base-swedish-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/KBLab/bert-base-swedish-cased/resolve/main/pytorch_model.bin
501 MB
- Xet hash:
- 952bff09211198e1d3d336b38018ff35c1d2cf809cfb544ef2edb982bc5dc8ce
- Size of remote file:
- 501 MB
- SHA256:
- 47169398946aa3e4652b78f790f68e00c12ed184e7de3d7d42e655a658e6987e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.