---
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