Instructions to use claudiapreda/deBerta-class_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use claudiapreda/deBerta-class_v1 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://claudiapreda/deBerta-class_v1") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from claudiapreda/deBerta-class_v1: direct link, hf CLI and curl.
- Browser
- Download file 8.57 MB
-
https://huggingface.co/claudiapreda/deBerta-class_v1/resolve/main/saved_model.pb
- Command line
-
hf download hf://claudiapreda/deBerta-class_v1/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/claudiapreda/deBerta-class_v1/resolve/main/saved_model.pb
8.57 MB
- Xet hash:
- ea08deb3563d424911fc97961d64f536e33f0839b09c31f85b986729bc0f3dc4
- Size of remote file:
- 8.57 MB
- SHA256:
- f08f3ba6cb24e30af5b7d829d61985870831b1efd7e330f8a2c69c2d3cecd5eb
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