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:
- ceef70ee5c068f2e8ec2ea787d1566d2f5846b612f0e0a3879edffa246dc91cf
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.