Instructions to use jgrc3/pfeiffer_adapter_classification_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use jgrc3/pfeiffer_adapter_classification_trained with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") model.load_adapter("jgrc3/pfeiffer_adapter_classification_trained", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model_head.bin from jgrc3/pfeiffer_adapter_classification_trained: direct link, hf CLI and curl.
- Browser
- Download file 2.37 MB
-
https://huggingface.co/jgrc3/pfeiffer_adapter_classification_trained/resolve/main/pytorch_model_head.bin
- Command line
-
hf download hf://jgrc3/pfeiffer_adapter_classification_trained/pytorch_model_head.bin
-
curl -L -o pytorch_model_head.bin https://huggingface.co/jgrc3/pfeiffer_adapter_classification_trained/resolve/main/pytorch_model_head.bin
2.37 MB
- Xet hash:
- c4abbdc7081d2734d8fb5d24a4ff391a31781e66810c0c54908f98f6cb9f40b9
- Size of remote file:
- 2.37 MB
- SHA256:
- 271f035dce4798b85fca96105e0c62ecec94279996ecd45ad09006558bf5b3be
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