Instructions to use BigTMiami/tapt_seq_bn_amazon_helpfulness_classification_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use BigTMiami/tapt_seq_bn_amazon_helpfulness_classification_adapter with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") model.load_adapter("BigTMiami/tapt_seq_bn_amazon_helpfulness_classification_adapter", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model_head.bin from BigTMiami/tapt_seq_bn_amazon_helpfulness_classification_adapter: direct link, hf CLI and curl.
- Browser
- Download file 2.37 MB
-
https://huggingface.co/BigTMiami/tapt_seq_bn_amazon_helpfulness_classification_adapter/resolve/main/pytorch_model_head.bin
- Command line
-
hf download hf://BigTMiami/tapt_seq_bn_amazon_helpfulness_classification_adapter/pytorch_model_head.bin
-
curl -L -o pytorch_model_head.bin https://huggingface.co/BigTMiami/tapt_seq_bn_amazon_helpfulness_classification_adapter/resolve/main/pytorch_model_head.bin
2.37 MB
- Xet hash:
- aa946e9aef4409c6ca0ae5a9450117d3cc3738d25e047ddf79819c887a4404fb
- Size of remote file:
- 2.37 MB
- SHA256:
- 93993faaacb6349e6e2434881f56d849f5621a904107c29068402db8a56a3bde
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.