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

# content

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: 0.4451
- Accuracy: 0.7772
- F1-score: 0.7788
- Recall: 0.8551
- Precision: 0.7150

## 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: 2.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1-score | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 0.5156        | 0.3814 | 500  | 0.4764          | 0.7687   | 0.7744   | 0.8972 | 0.6812    |
| 0.4498        | 0.7628 | 1000 | 0.4483          | 0.7790   | 0.7755   | 0.8622 | 0.7045    |
| 0.4198        | 1.1442 | 1500 | 0.4574          | 0.7745   | 0.7723   | 0.8642 | 0.6980    |
| 0.3687        | 1.5256 | 2000 | 0.4933          | 0.7696   | 0.7479   | 0.7723 | 0.7250    |
| 0.3591        | 1.9069 | 2500 | 0.4475          | 0.7902   | 0.7828   | 0.8545 | 0.7223    |
| 0.2809        | 2.2883 | 3000 | 0.5172          | 0.7696   | 0.7397   | 0.7400 | 0.7395    |
| 0.2712        | 2.6697 | 3500 | 0.5308          | 0.7799   | 0.7749   | 0.8564 | 0.7076    |
| 0.2482        | 3.0511 | 4000 | 0.6287          | 0.7622   | 0.7224   | 0.6992 | 0.7471    |
| 0.172         | 3.4325 | 4500 | 0.6831          | 0.7725   | 0.7491   | 0.7678 | 0.7314    |
| 0.1802        | 3.8139 | 5000 | 0.7141          | 0.7762   | 0.7570   | 0.7878 | 0.7285    |
| 0.1477        | 4.1953 | 5500 | 0.8481          | 0.7653   | 0.7444   | 0.7723 | 0.7184    |
| 0.121         | 4.5767 | 6000 | 0.9831          | 0.7639   | 0.7461   | 0.7840 | 0.7117    |
| 0.1377        | 4.9580 | 6500 | 0.9748          | 0.7662   | 0.7435   | 0.7658 | 0.7224    |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1