Instructions to use NbAiLab/nb-bert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/nb-bert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/nb-bert-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NbAiLab/nb-bert-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/nb-bert-large: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/NbAiLab/nb-bert-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/nb-bert-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/nb-bert-large/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- 4587868721275a70329caed80a60bcf8b5c90d786cd8c57bf4d6d920cc938f48
- Size of remote file:
- 1.42 GB
- SHA256:
- cadfaa8c0fc1b4e943d6580e15485f0e9c8193c5ad47f5330e24ab07d3895880
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