---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERTimbau-base_LeNER-Br
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: lener_br
      type: lener_br
      config: lener_br
      split: validation
      args: lener_br
    metrics:
    - name: Precision
      type: precision
      value: 0.8317805383022774
    - name: Recall
      type: recall
      value: 0.8839383938393839
    - name: F1
      type: f1
      value: 0.8570666666666666
    - name: Accuracy
      type: accuracy
      value: 0.9754369390647142
---

<!-- 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. -->

# BERTimbau-base_LeNER-Br

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8318
- Recall: 0.8839
- F1: 0.8571
- Accuracy: 0.9754

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2037        | 1.0   | 979  | nan             | 0.7910    | 0.8762 | 0.8314 | 0.9721   |
| 0.0308        | 2.0   | 1958 | nan             | 0.7747    | 0.8663 | 0.8180 | 0.9698   |
| 0.02          | 3.0   | 2937 | nan             | 0.8316    | 0.8911 | 0.8603 | 0.9801   |
| 0.0133        | 4.0   | 3916 | nan             | 0.8038    | 0.8812 | 0.8407 | 0.9728   |
| 0.0111        | 5.0   | 4895 | nan             | 0.8253    | 0.8707 | 0.8474 | 0.9753   |
| 0.0078        | 6.0   | 5874 | nan             | 0.8235    | 0.8779 | 0.8498 | 0.9711   |
| 0.0057        | 7.0   | 6853 | nan             | 0.8174    | 0.8768 | 0.8461 | 0.9760   |
| 0.0032        | 8.0   | 7832 | nan             | 0.8113    | 0.8845 | 0.8463 | 0.9769   |
| 0.0027        | 9.0   | 8811 | nan             | 0.8344    | 0.8867 | 0.8597 | 0.9767   |
| 0.0023        | 10.0  | 9790 | nan             | 0.8318    | 0.8839 | 0.8571 | 0.9754   |

### Testing results
metrics={'test_loss': 0.0710107609629631, 'test_precision': 0.8785578747628083, 'test_recall': 0.9138157894736842, 'test_f1': 0.8958400515962593, 'test_accuracy': 0.9884423662270061, 'test_runtime': 12.4395, 'test_samples_per_second': 111.741, 'test_steps_per_second': 13.988})
### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1