Instructions to use AdapterHub/roberta-base-pf-winogrande with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use AdapterHub/roberta-base-pf-winogrande with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") model.load_adapter("AdapterHub/roberta-base-pf-winogrande", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model_head.bin from AdapterHub/roberta-base-pf-winogrande: direct link, hf CLI and curl.
- Browser
- Download file 2.37 MB
-
https://huggingface.co/AdapterHub/roberta-base-pf-winogrande/resolve/main/pytorch_model_head.bin
- Command line
-
hf download hf://AdapterHub/roberta-base-pf-winogrande/pytorch_model_head.bin
-
curl -L -o pytorch_model_head.bin https://huggingface.co/AdapterHub/roberta-base-pf-winogrande/resolve/main/pytorch_model_head.bin
2.37 MB
- Xet hash:
- 91d99ccb4bbcbc336210bc60d9b7504f053aab3f37e20277e3ce2aaea23c39ba
- Size of remote file:
- 2.37 MB
- SHA256:
- 1293771a10f5d5e44a5e9bace46c887d61efe9b3f1bb19991496dda096074798
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.