Instructions to use AdapterHub/bert-base-uncased-pf-record with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use AdapterHub/bert-base-uncased-pf-record with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") model.load_adapter("AdapterHub/bert-base-uncased-pf-record", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model_head.bin from AdapterHub/bert-base-uncased-pf-record: direct link, hf CLI and curl.
- Browser
- Download file 2.37 MB
-
https://huggingface.co/AdapterHub/bert-base-uncased-pf-record/resolve/main/pytorch_model_head.bin
- Command line
-
hf download hf://AdapterHub/bert-base-uncased-pf-record/pytorch_model_head.bin
-
curl -L -o pytorch_model_head.bin https://huggingface.co/AdapterHub/bert-base-uncased-pf-record/resolve/main/pytorch_model_head.bin
2.37 MB
- Xet hash:
- 28ec324375c5daac2ee74ee5e1d63ba52fd739b5018f78c1e06418c674a127fb
- Size of remote file:
- 2.37 MB
- SHA256:
- 80087d6015d70eb356b399cca10761f71a7a968f82a8ac86674667e155b33acc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.