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

• Train Loss: 0.1142
• Train Accuracy: 0.9620
• Validation Loss: 0.2475
• Validation Accuracy: 0.9251
• 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': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0}
• training_precision: float32

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
2.3198	0.4600	0.9640	0.7095	0
0.7134	0.7711	0.4776	0.8398	1
0.3989	0.8623	0.3416	0.8799	2
0.2743	0.9020	0.3349	0.8782	3
0.2236	0.9180	0.2873	0.9015	4
0.1902	0.9308	0.2535	0.9101	5
0.1593	0.9433	0.2579	0.9210	6
0.1489	0.9492	0.2521	0.9233	7
0.1298	0.9559	0.2742	0.9166	8
0.1142	0.9620	0.2475	0.9251	9

Framework versions

• Transformers 4.51.3
• TensorFlow 2.18.0
• Datasets 3.6.0
• Tokenizers 0.21.1