---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased_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: 0.6604303086997194
    - name: Recall
      type: recall
      value: 0.7771051183269125
    - name: F1
      type: f1
      value: 0.7140328697850823
    - name: Accuracy
      type: accuracy
      value: 0.964795971887129
---

<!-- 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. -->

# bert-base-cased_LeNER-Br

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.6604
- Recall: 0.7771
- F1: 0.7140
- Accuracy: 0.9648

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2605        | 1.0   | 979  | nan             | 0.5248    | 0.6918 | 0.5969 | 0.9538   |
| 0.0541        | 2.0   | 1958 | nan             | 0.5968    | 0.7193 | 0.6524 | 0.9574   |
| 0.0327        | 3.0   | 2937 | nan             | 0.5566    | 0.7413 | 0.6358 | 0.9584   |
| 0.0216        | 4.0   | 3916 | nan             | 0.6642    | 0.7534 | 0.7060 | 0.9624   |
| 0.0175        | 5.0   | 4895 | nan             | 0.6391    | 0.7711 | 0.6989 | 0.9659   |
| 0.0095        | 6.0   | 5874 | nan             | 0.6099    | 0.7744 | 0.6823 | 0.9585   |
| 0.0099        | 7.0   | 6853 | nan             | 0.6474    | 0.7942 | 0.7133 | 0.9642   |
| 0.0056        | 8.0   | 7832 | nan             | 0.6606    | 0.7925 | 0.7205 | 0.9655   |
| 0.0038        | 9.0   | 8811 | nan             | 0.6547    | 0.7859 | 0.7144 | 0.9660   |
| 0.0035        | 10.0  | 9790 | nan             | 0.6604    | 0.7771 | 0.7140 | 0.9648   |


### Framework versions

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

### Testing results
metrics={'test_loss': 0.11072904616594315, 'test_precision': 0.7897691827822833, 'test_recall': 0.8423153692614771, 'test_f1': 0.8151963940759821, 'test_accuracy': 0.9825182903350019, 'test_runtime': 18.686, 'test_samples_per_second': 74.387, 'test_steps_per_second': 9.312})