Instructions to use BigTMiami/micro_seq_bn_helpfulness_classification_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use BigTMiami/micro_seq_bn_helpfulness_classification_adapter with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") model.load_adapter("BigTMiami/micro_seq_bn_helpfulness_classification_adapter", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model_head.bin from BigTMiami/micro_seq_bn_helpfulness_classification_adapter: direct link, hf CLI and curl.
- Browser
- Download file 2.37 MB
-
https://huggingface.co/BigTMiami/micro_seq_bn_helpfulness_classification_adapter/resolve/main/pytorch_model_head.bin
- Command line
-
hf download hf://BigTMiami/micro_seq_bn_helpfulness_classification_adapter/pytorch_model_head.bin
-
curl -L -o pytorch_model_head.bin https://huggingface.co/BigTMiami/micro_seq_bn_helpfulness_classification_adapter/resolve/main/pytorch_model_head.bin
2.37 MB
- Xet hash:
- 07a0a910743e5a77300b29bd75c87688aee6851314cc73f670ae0344c1a813e0
- Size of remote file:
- 2.37 MB
- SHA256:
- 8a275ebb897508594a5190c038987fbddd9ea3ad2132a9a81d40046ce4889be0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.