Instructions to use KBLab/bert-base-swedish-cased-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/bert-base-swedish-cased-new with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="KBLab/bert-base-swedish-cased-new")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("KBLab/bert-base-swedish-cased-new") model = AutoModelForMaskedLM.from_pretrained("KBLab/bert-base-swedish-cased-new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from KBLab/bert-base-swedish-cased-new: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/KBLab/bert-base-swedish-cased-new/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://KBLab/bert-base-swedish-cased-new/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/KBLab/bert-base-swedish-cased-new/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- dfc5abc1d692fd836a907a957d4948039cda26c29af2a765b0d61fb2ee0673f5
- Size of remote file:
- 541 MB
- SHA256:
- ee825a1ae2ff7980a147dd2e8ab8dc1180c37bffed87704005a1fd8f41a2268a
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