Instructions to use claudiapreda/deBerta-rqa-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use claudiapreda/deBerta-rqa-v8 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-v8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05629688d8065673a8ddab12132818062a6064995c299cab18d6058407cba66b
- Size of remote file:
- 8.57 MB
- SHA256:
- 1607d9b1dcc882df09fa8392e6d8b1ced671807062904ad05359b165cb4260da
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