Instructions to use Addedk/kbbert-distilled-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Addedk/kbbert-distilled-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Addedk/kbbert-distilled-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Addedk/kbbert-distilled-cased") model = AutoModelForMaskedLM.from_pretrained("Addedk/kbbert-distilled-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c4e660d15bbd611b116678910491a1d9731b557dae44be5b61f75e529c28256
- Size of remote file:
- 329 MB
- SHA256:
- 3af941dc259f6c6d70f078a88a7352dd25bb66f1ba69a2b84996d7419431b638
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