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_model_head.bin from emvecchi/socc_constructiveness: direct link, hf CLI and curl.
- Browser
- Download file 2.37 MB
-
https://huggingface.co/emvecchi/socc_constructiveness/resolve/main/pytorch_model_head.bin
- Command line
-
hf download hf://emvecchi/socc_constructiveness/pytorch_model_head.bin
-
curl -L -o pytorch_model_head.bin https://huggingface.co/emvecchi/socc_constructiveness/resolve/main/pytorch_model_head.bin
2.37 MB
- Xet hash:
- af73b55c1dba033166bcd3fa51498ab7925b7837f49d01293c342b27bf95efd4
- Size of remote file:
- 2.37 MB
- SHA256:
- 0dc9e7fec9c3e106519d9afe7ca729318f8b1a71b86456f46f23e4ca9b76535f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.