Instructions to use Randomui/dandg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Randomui/dandg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Randomui/dandg")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Randomui/dandg") model = AutoModelForTokenClassification.from_pretrained("Randomui/dandg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82413a6f1ddd1a0351a4460a86e506d3d74d4bab2037be036efc53b988af26e7
- Size of remote file:
- 4.09 kB
- SHA256:
- a01f2ac24daa9e3820630b10efcb383ba330fd6187da359c9b8d8b17550942a8
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