Instructions to use vargr/description-grader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use vargr/description-grader with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://vargr/description-grader") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74ade6ba56b3af147f7e01886ac753f5396c8cd6703c75f35ef5afaa20077e77
- Size of remote file:
- 12.7 MB
- SHA256:
- a029da27934b8feff28ec25795e52f1627d4b18b05efb30cc7e4d74567321339
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.