Instructions to use BigTMiami/adapter_classifier_from_prtrained_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use BigTMiami/adapter_classifier_from_prtrained_test with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") model.load_adapter("BigTMiami/adapter_classifier_from_prtrained_test", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model_head.bin from BigTMiami/adapter_classifier_from_prtrained_test: direct link, hf CLI and curl.
- Browser
- Download file 2.37 MB
-
https://huggingface.co/BigTMiami/adapter_classifier_from_prtrained_test/resolve/main/pytorch_model_head.bin
- Command line
-
hf download hf://BigTMiami/adapter_classifier_from_prtrained_test/pytorch_model_head.bin
-
curl -L -o pytorch_model_head.bin https://huggingface.co/BigTMiami/adapter_classifier_from_prtrained_test/resolve/main/pytorch_model_head.bin
2.37 MB
- Xet hash:
- 96784fca2571e2a4354f7986b906cfcb1bc4bae307ce562f5848a37e2a8598be
- Size of remote file:
- 2.37 MB
- SHA256:
- 8b3708e12db5a36619f99891ad483f7acc11fb93a372e68a496697768327d32a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.