Instructions to use NbAiLab/nb-roberta-base-ext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-roberta-base-ext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/nb-roberta-base-ext")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-roberta-base-ext") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/nb-roberta-base-ext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/nb-roberta-base-ext: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/NbAiLab/nb-roberta-base-ext/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/nb-roberta-base-ext/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/nb-roberta-base-ext/resolve/main/flax_model.msgpack
1.11 GB
- Xet hash:
- 92b6f8e274bed611b482fe3624a4c16340b2138657ffce747b4e55356ffb01cd
- Size of remote file:
- 1.11 GB
- SHA256:
- bb95b6f6a0ab40ce50ae03ce0cff81316742dff29abeedf14379683a06859b8d
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