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

Train Loss	Train Sparse Categorical Accuracy	Validation Loss	Validation Sparse Categorical Accuracy	Epoch
0.4908	0.8335	0.2872	0.9060	0
0.2496	0.9180	0.3137	0.8978	1
0.1947	0.9351	0.3234	0.9062	2
0.1597	0.9483	0.3092	0.9087	3
0.1304	0.9580	0.2928	0.9140	4
0.1013	0.9684	0.3450	0.9143	5
0.0785	0.9742	0.3590	0.9080	6
0.0709	0.9778	0.3711	0.9057	7
0.0541	0.9821	0.4010	0.9128	8
0.0506	0.9844	0.4108	0.9068	9

Framework versions

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