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:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://vargr/description-grader") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from vargr/description-grader: direct link, hf CLI and curl.
- Browser
- Download file 12.7 MB
-
https://huggingface.co/vargr/description-grader/resolve/refs%2Fpr%2F1/saved_model.pb
- Command line
-
hf download hf://vargr/description-grader@refs/pr/1/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/vargr/description-grader/resolve/refs%2Fpr%2F1/saved_model.pb
12.7 MB
- Xet hash:
- 4485b7983c7a068892831bebc1a3e33456810654f8c92cc989b5246d271642bc
- Size of remote file:
- 12.7 MB
- SHA256:
- f227367cdc12a60c013806576d7036c76e28791089c5cb2cbaa008fe60e4b7d8
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