Instructions to use AdapterHub/bert-base-uncased-pf-art with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use AdapterHub/bert-base-uncased-pf-art with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") model.load_adapter("AdapterHub/bert-base-uncased-pf-art", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model_head.bin from AdapterHub/bert-base-uncased-pf-art: direct link, hf CLI and curl.
- Browser
- Download file 2.37 MB
-
https://huggingface.co/AdapterHub/bert-base-uncased-pf-art/resolve/main/pytorch_model_head.bin
- Command line
-
hf download hf://AdapterHub/bert-base-uncased-pf-art/pytorch_model_head.bin
-
curl -L -o pytorch_model_head.bin https://huggingface.co/AdapterHub/bert-base-uncased-pf-art/resolve/main/pytorch_model_head.bin
2.37 MB
- Xet hash:
- afbc0c0684ff36f38463598f34639c3323096486e96fa625eead7e6e6d1a1a76
- Size of remote file:
- 2.37 MB
- SHA256:
- dc004c8a4e679de653361887c8c73b6674343b2759dc81c7f1ec670583b0a5f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.