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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3c7aaa76c89e13005e266e93e964e271d46dee9422c4d751c5aead998c0ac08
- Size of remote file:
- 709 MB
- SHA256:
- f6431f1d8fa2383bef0a83ea660dec09fd15dbfb9766a6cc16347d5df42451bb
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