Instructions to use merve/model-card-example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use merve/model-card-example with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://merve/model-card-example") - Notebooks
- Google Colab
- Kaggle
Model description
This model has couple of Dense layers.
Intended uses & limitations
It's intended to demonstrate capabilities of Hub for Keras on my blog post!
Training and evaluation data
It's trained on dummy data.
Above information is filled manually.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training Metrics
| Epochs | Train Loss | Validation Loss |
|---|---|---|
| 1 | 0.102 | 0.094 |
| 2 | 0.094 | 0.092 |
| 3 | 0.092 | 0.091 |
| 4 | 0.091 | 0.09 |
| 5 | 0.09 | 0.089 |
Model Plot
- Downloads last month
- -

# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://merve/model-card-example")