Instructions to use AdapterHub/bert-base-uncased-pf-swag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use AdapterHub/bert-base-uncased-pf-swag with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") model.load_adapter("AdapterHub/bert-base-uncased-pf-swag", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model_head.bin from AdapterHub/bert-base-uncased-pf-swag: direct link, hf CLI and curl.
- Browser
- Download file 2.37 MB
-
https://huggingface.co/AdapterHub/bert-base-uncased-pf-swag/resolve/main/pytorch_model_head.bin
- Command line
-
hf download hf://AdapterHub/bert-base-uncased-pf-swag/pytorch_model_head.bin
-
curl -L -o pytorch_model_head.bin https://huggingface.co/AdapterHub/bert-base-uncased-pf-swag/resolve/main/pytorch_model_head.bin
2.37 MB
- Xet hash:
- 49d8629786868eca43703ac52639af6509df05367f5a0bd2909a1df60eb7097c
- Size of remote file:
- 2.37 MB
- SHA256:
- 49d24aa68c4e65bc880eb2ad8d116776b11ead02001fa791429170e36c8461f3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.