Instructions to use vesteinn/DanskBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vesteinn/DanskBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vesteinn/DanskBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vesteinn/DanskBERT") model = AutoModelForMaskedLM.from_pretrained("vesteinn/DanskBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vesteinn/DanskBERT: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/vesteinn/DanskBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vesteinn/DanskBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vesteinn/DanskBERT/resolve/main/pytorch_model.bin
498 MB
- Xet hash:
- 97d44f5ef5aea1b7d31491ec46b3cb10c519f9a4d4845fb70d341c56a67a5a25
- Size of remote file:
- 498 MB
- SHA256:
- 6323593fb0cef67f7c069dce177f3b52a914bcd57169988a035fecb3f64de406
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