Instructions to use vesteinn/ScandiBERT-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vesteinn/ScandiBERT-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vesteinn/ScandiBERT-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vesteinn/ScandiBERT-NER") model = AutoModelForTokenClassification.from_pretrained("vesteinn/ScandiBERT-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vesteinn/ScandiBERT-NER: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/vesteinn/ScandiBERT-NER/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vesteinn/ScandiBERT-NER/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vesteinn/ScandiBERT-NER/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- a5e258c07705b9d22e5f3ffa2d477d4f46172d5422fa07ea0ef8c4abadaf0e6e
- Size of remote file:
- 496 MB
- SHA256:
- dfdf7b0ba493c1c138741a2de0607745faee7a47dc998776ed5aaeb6812a0077
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