This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4474
Precision Samples: 1.0
Recall Samples: 0.0
F1 Samples: 0.0
Precision Macro: 1.0
Recall Macro: 0.3636
F1 Macro: 0.3636
Precision Micro: 1.0
Recall Micro: 0.0
F1 Micro: 0.0
Precision Weighted: 1.0
Recall Weighted: 0.0
F1 Weighted: 0.0

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: 32
eval_batch_size: 32
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

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Samples	Recall Samples	F1 Samples	Precision Macro	Recall Macro	F1 Macro	Precision Micro	Recall Micro	F1 Micro	Precision Weighted	Recall Weighted	F1 Weighted
No log	1.0	5	0.6293	0.0783	0.3868	0.1220	0.4983	0.5865	0.3159	0.0751	0.375	0.1252	0.3532	0.375	0.1464
0.6408	2.0	10	0.5789	0.0787	0.2286	0.1079	0.7311	0.4717	0.3440	0.0839	0.2054	0.1192	0.5702	0.2054	0.0796
0.6408	3.0	15	0.5425	0.0708	0.0583	0.0554	0.9220	0.3953	0.3740	0.0706	0.0536	0.0609	0.8686	0.0536	0.0258
0.552	4.0	20	0.5135	0.1125	0.0271	0.0396	0.9759	0.3864	0.3719	0.0952	0.0357	0.0519	0.9634	0.0357	0.0110
0.552	5.0	25	0.4912	1.0	0.0	0.0	1.0	0.3636	0.3636	1.0	0.0	0.0	1.0	0.0	0.0
0.5007	6.0	30	0.4745	1.0	0.0	0.0	1.0	0.3636	0.3636	1.0	0.0	0.0	1.0	0.0	0.0
0.5007	7.0	35	0.4624	1.0	0.0	0.0	1.0	0.3636	0.3636	1.0	0.0	0.0	1.0	0.0	0.0
0.4713	8.0	40	0.4543	1.0	0.0	0.0	1.0	0.3636	0.3636	1.0	0.0	0.0	1.0	0.0	0.0
0.4713	9.0	45	0.4493	1.0	0.0	0.0	1.0	0.3636	0.3636	1.0	0.0	0.0	1.0	0.0	0.0
0.4567	10.0	50	0.4474	1.0	0.0	0.0	1.0	0.3636	0.3636	1.0	0.0	0.0	1.0	0.0	0.0

Framework versions

Transformers 4.46.0
Pytorch 2.3.1
Datasets 2.21.0
Tokenizers 0.20.1

g-assismoraes/bbau-semeval25_fold2

bbau-semeval25_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

g-assismoraes/
bbau-semeval25_fold2