---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-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: 0.765
    - name: Recall
      type: recall
      value: 0.8415841584158416
    - name: F1
      type: f1
      value: 0.8014667365112624
    - name: Accuracy
      type: accuracy
      value: 0.9711736213348917
---

<!-- 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. -->

# roberta-base_LeNER-Br

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.765
- Recall: 0.8416
- F1: 0.8015
- Accuracy: 0.9712

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.293         | 1.0   | 979  | nan             | 0.5758    | 0.7525 | 0.6524 | 0.9542   |
| 0.0596        | 2.0   | 1958 | nan             | 0.6546    | 0.7987 | 0.7195 | 0.9534   |
| 0.0376        | 3.0   | 2937 | nan             | 0.7366    | 0.8339 | 0.7822 | 0.9672   |
| 0.0256        | 4.0   | 3916 | nan             | 0.6975    | 0.8042 | 0.7471 | 0.9627   |
| 0.0192        | 5.0   | 4895 | nan             | 0.7173    | 0.8317 | 0.7702 | 0.9646   |
| 0.013         | 6.0   | 5874 | nan             | 0.7271    | 0.8498 | 0.7837 | 0.9605   |
| 0.013         | 7.0   | 6853 | nan             | 0.7426    | 0.8537 | 0.7943 | 0.9680   |
| 0.0064        | 8.0   | 7832 | nan             | 0.7493    | 0.8399 | 0.7920 | 0.9702   |
| 0.0052        | 9.0   | 8811 | nan             | 0.7611    | 0.8273 | 0.7928 | 0.9725   |
| 0.0044        | 10.0  | 9790 | nan             | 0.765     | 0.8416 | 0.8015 | 0.9712   |

### Testing results
metrics={'test_loss': 0.08161260932683945, 'test_precision': 0.8342714196372732, 'test_recall': 0.8840291583830351, 'test_f1': 0.8584298584298585, 'test_accuracy': 0.9863512377202157, 'test_runtime': 20.4317, 'test_samples_per_second': 68.032, 'test_steps_per_second': 8.516})

### Framework versions

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