Instructions to use rakgesh/onepiece_predictor_transfer_v01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use rakgesh/onepiece_predictor_transfer_v01 with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("rakgesh/onepiece_predictor_transfer_v01") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1956a787baf53dbd9e6c04ddfaa71e51918c6e7388bc7c4a07cbb0690344b742
- Size of remote file:
- 3.85 MB
- SHA256:
- e280fdd614b0eadad8d9fb006fed5cd0beeaa5b3adf32944e0b8ad4bbf887891
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.