Instructions to use jgrc3/seqbn_adapter_classification_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use jgrc3/seqbn_adapter_classification_trained with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") model.load_adapter("jgrc3/seqbn_adapter_classification_trained", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model_head.bin from jgrc3/seqbn_adapter_classification_trained: direct link, hf CLI and curl.
- Browser
- Download file 2.37 MB
-
https://huggingface.co/jgrc3/seqbn_adapter_classification_trained/resolve/main/pytorch_model_head.bin
- Command line
-
hf download hf://jgrc3/seqbn_adapter_classification_trained/pytorch_model_head.bin
-
curl -L -o pytorch_model_head.bin https://huggingface.co/jgrc3/seqbn_adapter_classification_trained/resolve/main/pytorch_model_head.bin
2.37 MB
- Xet hash:
- 5f207601034361b7159a32adad2a37784dfbf49de7030682a653cdb974e7169f
- Size of remote file:
- 2.37 MB
- SHA256:
- 8b416d3422261fdd6883049308ffcb981f6b2ac7661a6f5b56c0763ca0b6c6d2
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