bert-base-multilingual-cased-finetuned-lener_br-finetuned-lener-br

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

• Loss: nan (To update)
• Precision: 0.9122 (To update)
• Recall: 0.9163 (To update)
• F1: 0.9142 (To update)
• Accuracy: 0.9826 (To update)

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters (To update)

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15
• mixed_precision_training: Native AMP

Training results (To update)

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.068	1.0	3914	nan	0.6196	0.8604	0.7204	0.9568
0.0767	2.0	7828	nan	0.8270	0.8710	0.8484	0.9693
0.0257	3.0	11742	nan	0.7243	0.9005	0.8029	0.9639
0.0193	4.0	15656	nan	0.9010	0.8984	0.8997	0.9821
0.0156	5.0	19570	nan	0.7150	0.9121	0.8016	0.9641
0.0165	6.0	23484	nan	0.7640	0.8796	0.8177	0.9691
0.0225	7.0	27398	nan	0.8851	0.9098	0.8973	0.9803
0.016	8.0	31312	nan	0.9081	0.9015	0.9048	0.9792
0.0078	9.0	35226	nan	0.8941	0.8863	0.8902	0.9788
0.0061	10.0	39140	nan	0.9026	0.9002	0.9014	0.9804
0.0057	11.0	43054	nan	0.8793	0.9018	0.8904	0.9769
0.0044	12.0	46968	nan	0.8790	0.9033	0.8910	0.9785
0.0043	13.0	50882	nan	0.9122	0.9163	0.9142	0.9826
0.0003	14.0	54796	nan	0.9032	0.9070	0.9051	0.9807
0.0025	15.0	58710	nan	0.8903	0.9085	0.8993	0.9798

Framework versions (To update)

• Transformers 4.23.1
• Pytorch 1.12.1+cu113
• Datasets 2.6.1
• Tokenizers 0.13.1