Instructions to use fcfrank10/dbert_model_02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fcfrank10/dbert_model_02 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fcfrank10/dbert_model_02")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fcfrank10/dbert_model_02") model = AutoModelForTokenClassification.from_pretrained("fcfrank10/dbert_model_02") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b965b64b7e0998c7eb046100ba4f10f57651eb7200ad64e251bab9635e627424
- Size of remote file:
- 4.6 kB
- SHA256:
- 5570e4ec2811f2011ffd57873e1653a5ad2be6389a1d416bc357afbae1a2d87b
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