Instructions to use saattrupdan/nbailab-base-ner-scandi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saattrupdan/nbailab-base-ner-scandi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="saattrupdan/nbailab-base-ner-scandi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("saattrupdan/nbailab-base-ner-scandi") model = AutoModelForTokenClassification.from_pretrained("saattrupdan/nbailab-base-ner-scandi", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download model.safetensors from saattrupdan/nbailab-base-ner-scandi: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/saattrupdan/nbailab-base-ner-scandi/resolve/main/model.safetensors
- Command line
-
hf download hf://saattrupdan/nbailab-base-ner-scandi/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/saattrupdan/nbailab-base-ner-scandi/resolve/main/model.safetensors
709 MB
- Xet hash:
- d9359e90af41370918dc94f70ec4c0625ffd2935f86d1ca76b9e3876841a2eb6
- Size of remote file:
- 709 MB
- SHA256:
- 48678ef89be6257c8ebf89cf9fdff416ff2fa02adeaa7a99df4644c018577cf6
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