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:
- a6569197f81906852016fc13d2c53a98de7cf18d379dc32ace67666b21792618
- Size of remote file:
- 539 MB
- SHA256:
- 1af2bef1712ddbd5758d24060fd14872b9f8b6f3c7092f1db101af8549578f3a
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