pii_mbert_az

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

• Loss: 0.1319
• Precision: 0.8726
• Recall: 0.9026
• F1: 0.8874
• Accuracy: 0.9619

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:

• learning_rate: 3e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: reduce_lr_on_plateau
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	313	0.1464	0.8797	0.8615	0.8705	0.9587
0.2128	2.0	626	0.1319	0.8726	0.9026	0.8874	0.9619
0.2128	3.0	939	0.1461	0.8689	0.8924	0.8805	0.9596
0.0783	4.0	1252	0.1529	0.8837	0.9049	0.8942	0.9620
0.0443	5.0	1565	0.1921	0.8657	0.9157	0.8900	0.9615
0.0443	6.0	1878	0.1647	0.8975	0.9224	0.9098	0.9685
0.0201	7.0	2191	0.1725	0.8904	0.9183	0.9041	0.9674
0.0098	8.0	2504	0.1766	0.8917	0.9199	0.9056	0.9682
0.0098	9.0	2817	0.1756	0.8926	0.9202	0.9062	0.9686
0.007	10.0	3130	0.1763	0.8916	0.9189	0.9051	0.9684
0.007	11.0	3443	0.1772	0.8907	0.9183	0.9043	0.9682
0.007	12.0	3756	0.1773	0.8895	0.9173	0.9032	0.9680
0.0067	13.0	4069	0.1775	0.8892	0.9170	0.9029	0.9680
0.0067	14.0	4382	0.1775	0.8897	0.9170	0.9032	0.9679
0.0062	15.0	4695	0.1775	0.8897	0.9170	0.9032	0.9679

Framework versions

• Transformers 4.41.0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1