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:
- 3999246b8607ab0a48b8e33db80e2df46c34f7553bd65ff5b1e4ca2596b3fddd
- Size of remote file:
- 3.96 kB
- SHA256:
- 930c590961859a2b35ed2e157911856dbb959b176a2186675b5606638a69432c
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