Instructions to use NbAiLab/nb-roberta-base-ncc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-roberta-base-ncc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/nb-roberta-base-ncc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-roberta-base-ncc") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/nb-roberta-base-ncc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/nb-roberta-base-ncc: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/NbAiLab/nb-roberta-base-ncc/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/nb-roberta-base-ncc/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/nb-roberta-base-ncc/resolve/main/flax_model.msgpack
1.11 GB
- Xet hash:
- 7010a56aad4bf260a8a1dd42d8bec6a91f2e0ceeb91e4ce4eb4013186d1ccc5f
- Size of remote file:
- 1.11 GB
- SHA256:
- 360eaa6f33b0fae0b58a4195d795f731958d153dace2af65ff548df20b9aa20b
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