Instructions to use ShengdingHu/prefix_t5-base_mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShengdingHu/prefix_t5-base_mrpc with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ShengdingHu/prefix_t5-base_mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1173045d1137a65e4490a3dfd088e057f9f2108caa7237abace268a680899e37
- Size of remote file:
- 76.7 MB
- SHA256:
- 4372180c5ef90d5bb1cf8667490984dfd05bbd3c44b8e378129ab8cfa1ac4a52
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.