Instructions to use keras-io/ProbabalisticBayesianModel-Wine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use keras-io/ProbabalisticBayesianModel-Wine with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras-io/ProbabalisticBayesianModel-Wine") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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**Full credits go to [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)**
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## Training and evaluation data 🍷
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We use the wine quality dataset found [here](https://www.tensorflow.org/datasets/catalog/wine_quality). Each wine was scored from 0-10 by wine experts, and includes 11 physicochemical features about the wine.
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**Full credits go to [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)**
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## Using this model
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This repo contains model weights only. To use this model, refer to the following code contained in load_bnn_model.py.
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## Training and evaluation data 🍷
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We use the wine quality dataset found [here](https://www.tensorflow.org/datasets/catalog/wine_quality). Each wine was scored from 0-10 by wine experts, and includes 11 physicochemical features about the wine.
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