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---
library_name: transformers
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: BingoGuard-bert-base-base-plus-custom
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BingoGuard-bert-base-base-plus-custom
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6610
- Accuracy: 0.8766
- F1: 0.8745
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.381 | 1.0 | 67 | 0.2833 | 0.9 | 0.8920 |
| 0.2434 | 2.0 | 134 | 0.3236 | 0.8979 | 0.8943 |
| 0.1175 | 3.0 | 201 | 0.4126 | 0.8702 | 0.8737 |
| 0.06 | 4.0 | 268 | 0.6708 | 0.8426 | 0.852 |
| 0.0495 | 5.0 | 335 | 0.5486 | 0.8766 | 0.8728 |
| 0.0187 | 6.0 | 402 | 0.6512 | 0.8787 | 0.8779 |
| 0.0072 | 7.0 | 469 | 0.6511 | 0.8745 | 0.8709 |
| 0.0375 | 8.0 | 536 | 0.6610 | 0.8766 | 0.8745 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.4
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