ner-bert-lenerbr-v2

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

• Loss: 0.1931
• Precision: 0.8384
• Recall: 0.9092
• F1: 0.8724
• Accuracy: 0.9699

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0601	1.0	979	0.1134	0.8575	0.8516	0.8546	0.9715
0.0345	2.0	1958	0.1402	0.7896	0.9022	0.8421	0.9657
0.0243	3.0	2937	0.1350	0.8124	0.9060	0.8566	0.9696
0.0256	4.0	3916	0.1592	0.7624	0.9073	0.8286	0.9640
0.0143	5.0	4895	0.1951	0.8462	0.8983	0.8715	0.9678
0.0139	6.0	5874	0.1874	0.8252	0.9110	0.8660	0.9679
0.0051	7.0	6853	0.1685	0.8301	0.9049	0.8659	0.9692
0.0067	8.0	7832	0.1931	0.8384	0.9092	0.8724	0.9699
0.0018	9.0	8811	0.2004	0.8206	0.9110	0.8634	0.9692
0.0044	10.0	9790	0.2000	0.8295	0.9090	0.8674	0.9694

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.1
• Tokenizers 0.21.1