Instructions to use fcfrank10/dbert_model_03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fcfrank10/dbert_model_03 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fcfrank10/dbert_model_03")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fcfrank10/dbert_model_03") model = AutoModelForTokenClassification.from_pretrained("fcfrank10/dbert_model_03") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8687c4703ac96f12bc4c96c5642453e7af0574d73d153da3d3ebd9a73daa6f41
- Size of remote file:
- 4.6 kB
- SHA256:
- 889f699437c178a67eec5540b523d3f148958052cac0f159d1c9b688a0f6d09b
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