Instructions to use claudiapreda/deBerta-class_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use claudiapreda/deBerta-class_test with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://claudiapreda/deBerta-class_test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4c73079e008bced6050dfe8248913cc6f1f126ab5eedc272f0bd11c0e043f3b
- Size of remote file:
- 8.59 MB
- SHA256:
- 3f1566ac254da59b77734404807d622ee8b9661a737abadf764b80a62596d1e9
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