Instructions to use jannikskytt/MeDa-Bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jannikskytt/MeDa-Bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jannikskytt/MeDa-Bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jannikskytt/MeDa-Bert") model = AutoModelForMaskedLM.from_pretrained("jannikskytt/MeDa-Bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 660b4606f968c1c75a6a25a574199e136c0d4c000d84df5af404276d834951de
- Size of remote file:
- 443 MB
- SHA256:
- 765d4c831f3487e2172d259f4bb0b28cd48f1a067cefe7f22eacc8a7c458ce08
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