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 save_model.py from NbAiLab/nb-roberta-base-scandinavian: direct link, hf CLI and curl.
- Browser
- Download file 565 Bytes
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https://huggingface.co/NbAiLab/nb-roberta-base-scandinavian/resolve/main/save_model.py
- Command line
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hf download hf://NbAiLab/nb-roberta-base-scandinavian/save_model.py
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curl -L -o save_model.py https://huggingface.co/NbAiLab/nb-roberta-base-scandinavian/resolve/main/save_model.py
565 Bytes
| from transformers.modeling_flax_pytorch_utils import load_flax_checkpoint_in_pytorch_model | |
| from transformers import RobertaConfig, RobertaModel | |
| config = RobertaConfig.from_pretrained("./") | |
| model = RobertaModel(config) | |
| load_flax_checkpoint_in_pytorch_model(model, "./flax_model.msgpack") | |
| model.save_pretrained("./") | |
| from transformers import AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("./") | |
| tokenizer.save_pretrained("./") | |
| ('./tokenizer_config.json','./special_tokens_map.json', './vocab.json', './merges.txt','./added_tokens.json','./tokenizer.json') | |