Instructions to use BramVanroy/deberta-v3-base-uner-down200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BramVanroy/deberta-v3-base-uner-down200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BramVanroy/deberta-v3-base-uner-down200")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BramVanroy/deberta-v3-base-uner-down200") model = AutoModelForTokenClassification.from_pretrained("BramVanroy/deberta-v3-base-uner-down200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab0fab5ff020e2d5f72c44604ed690fa0ae19e8cb722ff2792337b65a2bcac06
- Size of remote file:
- 5.84 kB
- SHA256:
- f96543e84915d79809782baa8c853badf50ca8e32ef7a90e809196d1ba1b307e
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