---
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-pt
  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-pt

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: 0.1359
- Accuracy: 0.9438
- F1: 0.6724

## 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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4101        | 1.0   | 456  | 0.1396          | 0.9428   | 0.6398 |
| 0.3366        | 2.0   | 912  | 0.1291          | 0.9393   | 0.6516 |
| 0.2788        | 3.0   | 1368 | 0.1209          | 0.9462   | 0.6766 |
| 0.2695        | 4.0   | 1824 | 0.1321          | 0.9413   | 0.6648 |
| 0.236         | 5.0   | 2280 | 0.1359          | 0.9438   | 0.6724 |


### Framework versions

- Transformers 4.55.2
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.4