Instructions to use emvecchi/socc_constructiveness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use emvecchi/socc_constructiveness with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") model.load_adapter("emvecchi/socc_constructiveness", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_adapter.bin from emvecchi/socc_constructiveness: direct link, hf CLI and curl.
- Browser
- Download file 3.59 MB
-
https://huggingface.co/emvecchi/socc_constructiveness/resolve/main/pytorch_adapter.bin
- Command line
-
hf download hf://emvecchi/socc_constructiveness/pytorch_adapter.bin
-
curl -L -o pytorch_adapter.bin https://huggingface.co/emvecchi/socc_constructiveness/resolve/main/pytorch_adapter.bin
3.59 MB
- Xet hash:
- 9bf403b36f1f36824fa54b8fd76f3a391426e217da5d122d8764771d19000a42
- Size of remote file:
- 3.59 MB
- SHA256:
- f531e113dd2aabcf25fea30fc4b6b4410f6a3359eb432fb8e920683bf95f0d46
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.