Instructions to use fcfrank10/dbert_model_04 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fcfrank10/dbert_model_04 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fcfrank10/dbert_model_04")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fcfrank10/dbert_model_04") model = AutoModelForTokenClassification.from_pretrained("fcfrank10/dbert_model_04", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58729fc10499d261b02cce330fe6824de5b5ed8695f02f591a71b1678cd39c89
- Size of remote file:
- 4.6 kB
- SHA256:
- cb2fb352cfdd6d451b20a4d6d8d00303738b79cd1efa83ae65986f7891fac99f
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