Instructions to use KBLab/bert-base-swedish-cased-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KBLab/bert-base-swedish-cased-pos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KBLab/bert-base-swedish-cased-pos")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KBLab/bert-base-swedish-cased-pos") model = AutoModelForTokenClassification.from_pretrained("KBLab/bert-base-swedish-cased-pos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from KBLab/bert-base-swedish-cased-pos: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/KBLab/bert-base-swedish-cased-pos/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://KBLab/bert-base-swedish-cased-pos/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/KBLab/bert-base-swedish-cased-pos/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 5bca88655c9dde6ed1ecd45fd607bcb55905f82c0dca5ce46a5ff6c08244a3bb
- Size of remote file:
- 499 MB
- SHA256:
- 1489819997d54511d3fa26aef8067aab62b792716a382711716213b4d3d77d26
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