Instructions to use fcfrank10/dbert_model_05 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fcfrank10/dbert_model_05 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fcfrank10/dbert_model_05")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fcfrank10/dbert_model_05") model = AutoModelForTokenClassification.from_pretrained("fcfrank10/dbert_model_05") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a08311b0273735f2f9853b2e76a9fcf5485f76d613f2b8d62e77edb70b2dc628
- Size of remote file:
- 4.6 kB
- SHA256:
- 319bcbd166477bb86f1ed70077312cd21ac40476478d82b5ded5f15926d279f0
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