Instructions to use NbAiLab/roberta_jan_128_scandinavian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/roberta_jan_128_scandinavian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/roberta_jan_128_scandinavian")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/roberta_jan_128_scandinavian") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/roberta_jan_128_scandinavian") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4a5c24bc992a5bba6228453810c7683e3349787d8b07c48ae184979f505689d
- Size of remote file:
- 499 MB
- SHA256:
- 8be88fc1176831918009bffa0ef81d06c1d1cda5c6ccb0aecacc97af77028fe3
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