Instructions to use ranjankn/gpad-v1-full-taskA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ranjankn/gpad-v1-full-taskA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ranjankn/gpad-v1-full-taskA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb96408b024923ab4582d5c3ebc51b18ade10630dc6da763c2f6b7439ef6a2bb
- Size of remote file:
- 5.43 kB
- SHA256:
- afd8b06536d39c9fb0bbd4c1785476dd531143ed495497e92350cd5699210e18
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.