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

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1290
- Accuracy: 0.9517
- F1: 0.7135

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch 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     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.3873        | 1.0    | 1823 | 0.1427          | 0.9344   | 0.6598 |
| 0.3097        | 2.0    | 3646 | 0.1137          | 0.9457   | 0.6839 |
| 0.2555        | 3.0    | 5469 | 0.1281          | 0.9383   | 0.6736 |
| 0.2167        | 4.0    | 7292 | 0.1229          | 0.9507   | 0.7191 |
| 0.1873        | 4.9975 | 9110 | 0.1290          | 0.9517   | 0.7135 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.4