Instructions to use Arthur-Tsai/histv4_ftis_pretrain_tssp-smlm_0329 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arthur-Tsai/histv4_ftis_pretrain_tssp-smlm_0329 with Transformers:
# Load model directly from transformers import HiSenTrans model = HiSenTrans.from_pretrained("Arthur-Tsai/histv4_ftis_pretrain_tssp-smlm_0329", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e55518293f860e2c9e54a18267cea497a151175164b7e7e1d9ef44717cf39cbd
- Size of remote file:
- 54.6 MB
- SHA256:
- 6c67fffd5cb6d5fcb8940e864bb68e19d4bd6c96933bdccd7a9e4340b84fcce0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.