bertimbau-large-lener_br

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

• Loss: 0.1271
• Precision: 0.8965
• Recall: 0.9198
• F1: 0.9080
• Accuracy: 0.9801

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: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0674	1.0	1957	0.1349	0.7617	0.8710	0.8127	0.9594
0.0443	2.0	3914	0.1867	0.6862	0.9194	0.7858	0.9575
0.0283	3.0	5871	0.1185	0.8206	0.8766	0.8477	0.9678
0.0226	4.0	7828	0.1405	0.8072	0.8978	0.8501	0.9708
0.0141	5.0	9785	0.1898	0.7224	0.9194	0.8090	0.9629
0.01	6.0	11742	0.1655	0.9062	0.8856	0.8958	0.9741
0.012	7.0	13699	0.1271	0.8965	0.9198	0.9080	0.9801
0.0091	8.0	15656	0.1919	0.8890	0.8886	0.8888	0.9719
0.0042	9.0	17613	0.1725	0.8977	0.8985	0.8981	0.9744
0.0043	10.0	19570	0.1530	0.8878	0.9034	0.8955	0.9761
0.0042	11.0	21527	0.1635	0.8792	0.9108	0.8947	0.9774
0.0033	12.0	23484	0.2009	0.8155	0.9138	0.8619	0.9719
0.0008	13.0	25441	0.1766	0.8737	0.9135	0.8932	0.9755
0.0005	14.0	27398	0.1868	0.8616	0.9129	0.8865	0.9743
0.0014	15.0	29355	0.1910	0.8694	0.9101	0.8893	0.9746

Framework versions

• Transformers 4.8.2
• Pytorch 1.9.0+cu102
• Datasets 1.9.0
• Tokenizers 0.10.3