---
library_name: transformers
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: BingoGuard-bert-large-base-benchmarks
  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-large-base-benchmarks

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/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