BingoGuard-bert-large-base-benchmarks

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: 0.9187
• Accuracy: 0.8026
• F1: 0.7892

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.4037	1.0	856	0.3850	0.8282	0.8331
0.3242	2.0	1712	0.3815	0.8346	0.8346
0.2615	3.0	2568	0.4259	0.8307	0.8318
0.2397	4.0	3424	0.4942	0.8287	0.8245
0.2114	5.0	4280	0.5433	0.8243	0.8209
0.1708	6.0	5136	0.6266	0.8180	0.8087
0.1375	7.0	5992	0.7617	0.8115	0.8015
0.1066	8.0	6848	0.9187	0.8026	0.7892

Framework versions

• Transformers 4.55.4
• Pytorch 2.8.0+cu128
• Datasets 3.6.0
• Tokenizers 0.21.4