Instructions to use NbAiLab/nb-roberta-base-scandi-1e4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-roberta-base-scandi-1e4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/nb-roberta-base-scandi-1e4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-roberta-base-scandi-1e4") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/nb-roberta-base-scandi-1e4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/nb-roberta-base-scandi-1e4: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/NbAiLab/nb-roberta-base-scandi-1e4/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/nb-roberta-base-scandi-1e4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/nb-roberta-base-scandi-1e4/resolve/main/flax_model.msgpack
1.11 GB
- Xet hash:
- a11794d172f44e662a18502d8967f6fcceae6119c2345df8c55116471063af2a
- Size of remote file:
- 1.11 GB
- SHA256:
- 3491691116e3d7128ea47bce7bb46a7947c13226aea31d9098520d884f995fbb
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