Instructions to use claudiapreda/deBerta-rqa-v3.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use claudiapreda/deBerta-rqa-v3.1 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-rqa-v3.1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5f8434cb8ce0e325c1f8ece46278f217b261faef268c70680f7748545767f43
- Size of remote file:
- 8.57 MB
- SHA256:
- 9bde951f66590ed6276e4791aa677ba9984a85808cedcd377c208804612c323a
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