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_adapter.bin from jgrc3/pfeiffer_adapter_classification_trained: direct link, hf CLI and curl.
- Browser
- Download file 3.6 MB
-
https://huggingface.co/jgrc3/pfeiffer_adapter_classification_trained/resolve/main/pytorch_adapter.bin
- Command line
-
hf download hf://jgrc3/pfeiffer_adapter_classification_trained/pytorch_adapter.bin
-
curl -L -o pytorch_adapter.bin https://huggingface.co/jgrc3/pfeiffer_adapter_classification_trained/resolve/main/pytorch_adapter.bin
3.6 MB
- Xet hash:
- 2c3bc1d1683b1aa26d4d718426d0e61070c210aaa1c3c6a90bb88605b5762fdc
- Size of remote file:
- 3.6 MB
- SHA256:
- 40d970f063bb4c52f2c21e8ddc78f4122f276bd4a7425c8aa6e68bcb18fb8af9
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