Instructions to use joycerlz/distilbert-personality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use joycerlz/distilbert-personality with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://joycerlz/distilbert-personality") - Notebooks
- Google Colab
- Kaggle
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
|---|---|
| name | Adam |
| learning_rate | 2.9999999242136255e-05 |
| decay | 0.0 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
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