---
library_name: transformers
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: neuralmind/bert-base-portuguese-cased
  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. -->

# neuralmind/bert-base-portuguese-cased

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: 0.0460
- Accuracy: 0.8367
- F1: 0.7871
- Recall: 0.8194
- Precision: 0.7687

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 5151
- 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
- lr_scheduler_warmup_steps: 150
- num_epochs: 19

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.0801        | 1.0   | 18   | 0.0769          | 0.7411   | 0.4256 | 0.5    | 0.3705    |
| 0.0691        | 2.0   | 36   | 0.0709          | 0.75     | 0.4612 | 0.5172 | 0.8739    |
| 0.0647        | 3.0   | 54   | 0.0661          | 0.75     | 0.4612 | 0.5172 | 0.8739    |
| 0.0644        | 4.0   | 72   | 0.0648          | 0.6518   | 0.5774 | 0.5856 | 0.5753    |
| 0.0621        | 5.0   | 90   | 0.0632          | 0.7054   | 0.6424 | 0.6554 | 0.6367    |
| 0.0621        | 6.0   | 108  | 0.0627          | 0.7232   | 0.6265 | 0.6226 | 0.6319    |
| 0.0586        | 7.0   | 126  | 0.0595          | 0.75     | 0.6937 | 0.7079 | 0.6857    |
| 0.0547        | 8.0   | 144  | 0.0582          | 0.7768   | 0.7338 | 0.7597 | 0.7223    |
| 0.0509        | 9.0   | 162  | 0.0554          | 0.7768   | 0.7338 | 0.7597 | 0.7223    |
| 0.0462        | 10.0  | 180  | 0.0557          | 0.75     | 0.7091 | 0.7416 | 0.6998    |
| 0.0437        | 11.0  | 198  | 0.0532          | 0.7768   | 0.7382 | 0.7709 | 0.7264    |
| 0.0415        | 12.0  | 216  | 0.0515          | 0.7857   | 0.7466 | 0.7769 | 0.7341    |
| 0.0356        | 13.0  | 234  | 0.0545          | 0.8036   | 0.7547 | 0.7665 | 0.7461    |
| 0.0301        | 14.0  | 252  | 0.0543          | 0.8214   | 0.7770 | 0.7898 | 0.7675    |
| 0.0262        | 15.0  | 270  | 0.0541          | 0.8036   | 0.7594 | 0.7777 | 0.7481    |
| 0.0248        | 16.0  | 288  | 0.0583          | 0.8125   | 0.7584 | 0.7613 | 0.7557    |
| 0.0232        | 17.0  | 306  | 0.0593          | 0.8125   | 0.7635 | 0.7725 | 0.7562    |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0