robbery_dataset_tf_finetuned_20221113

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robbery_dataset_tf_finetuned_20221113

This model is a fine-tuned version of distilbert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.0506
• Train Sparse Categorical Accuracy: 0.9844
• Validation Loss: 0.4108
• Validation Sparse Categorical Accuracy: 0.9068
• Epoch: 9

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:

• optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
• training_precision: float32

Training results

Framework versions

• Transformers 4.24.0
• TensorFlow 2.9.2
• Datasets 2.6.1
• Tokenizers 0.13.2