Instructions to use NbAiLab/nb-roberta-base-scandinavian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-roberta-base-scandinavian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/nb-roberta-base-scandinavian")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-roberta-base-scandinavian") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/nb-roberta-base-scandinavian", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/nb-roberta-base-scandinavian: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/NbAiLab/nb-roberta-base-scandinavian/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/nb-roberta-base-scandinavian/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/nb-roberta-base-scandinavian/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 234ec55dfd3ad16d617c60de04659418289e884acb26447cdb5a3ced134863bd
- Size of remote file:
- 499 MB
- SHA256:
- 64f7ff612ca7eb3abfe9ae1d2c0088df78d9a5ab1af89b2368ceabadf34e46d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.