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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fd71a9d0706fd3680b5fe015928fed27d20b28d9d9d6bdb908553fd578ee49c
- Size of remote file:
- 539 MB
- SHA256:
- 4b4e2961bce17b5769f4086af6fff76e92b3398114284be6427390eb9fae9ba5
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