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")# 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
- Xet hash:
- f52cd88394b5242561dd671671bae54ee3cbdfb5a26050eec23f86caa9078bec
- Size of remote file:
- 2.67 kB
- SHA256:
- 2db68c7bf76cfd21c0f70c0ed31dbc2277bf061d0bf39ff2967bf2bc6d8d7dbe
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