Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2605	1.0	979	nan	0.5248	0.6918	0.5969	0.9538
0.0541	2.0	1958	nan	0.5968	0.7193	0.6524	0.9574
0.0327	3.0	2937	nan	0.5566	0.7413	0.6358	0.9584
0.0216	4.0	3916	nan	0.6642	0.7534	0.7060	0.9624
0.0175	5.0	4895	nan	0.6391	0.7711	0.6989	0.9659
0.0095	6.0	5874	nan	0.6099	0.7744	0.6823	0.9585
0.0099	7.0	6853	nan	0.6474	0.7942	0.7133	0.9642
0.0056	8.0	7832	nan	0.6606	0.7925	0.7205	0.9655
0.0038	9.0	8811	nan	0.6547	0.7859	0.7144	0.9660
0.0035	10.0	9790	nan	0.6604	0.7771	0.7140	0.9648

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Testing results

metrics={'test_loss': 0.11072904616594315, 'test_precision': 0.7897691827822833, 'test_recall': 0.8423153692614771, 'test_f1': 0.8151963940759821, 'test_accuracy': 0.9825182903350019, 'test_runtime': 18.686, 'test_samples_per_second': 74.387, 'test_steps_per_second': 9.312})

CassioBN/bert-base-cased_LeNER-Br

bert-base-cased_LeNER-Br

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Testing results

CassioBN/
bert-base-cased_LeNER-Br