Instructions to use scampion/EUBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scampion/EUBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="scampion/EUBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("scampion/EUBERT") model = AutoModelForMaskedLM.from_pretrained("scampion/EUBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21c4a6419b050daf9b94a74d6928cebbf465e693eca047e1989ace27ee10bcad
- Size of remote file:
- 376 MB
- SHA256:
- 35157d91606d022b90e23850a025295fc40baa789ce1d3e5ea2345d599be6703
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