---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-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: 0.8295165394402035
    - name: Recall
      type: recall
      value: 0.8965896589658966
    - name: F1
      type: f1
      value: 0.8617499339148824
    - name: Accuracy
      type: accuracy
      value: 0.9714166181062949
---

<!-- 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. -->

# xlm-roberta-base_LeNER-Br

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8295
- Recall: 0.8966
- F1: 0.8617
- Accuracy: 0.9714

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2394        | 1.0   | 979  | nan             | 0.7134    | 0.8614 | 0.7805 | 0.9638   |
| 0.0375        | 2.0   | 1958 | nan             | 0.8035    | 0.9043 | 0.8509 | 0.9670   |
| 0.0256        | 3.0   | 2937 | nan             | 0.8026    | 0.8878 | 0.8430 | 0.9761   |
| 0.0194        | 4.0   | 3916 | nan             | 0.7836    | 0.8861 | 0.8317 | 0.9670   |
| 0.015         | 5.0   | 4895 | nan             | 0.8061    | 0.8988 | 0.8499 | 0.9691   |
| 0.0098        | 6.0   | 5874 | nan             | 0.8279    | 0.9076 | 0.8659 | 0.9715   |
| 0.0082        | 7.0   | 6853 | nan             | 0.8067    | 0.8905 | 0.8465 | 0.9681   |
| 0.0042        | 8.0   | 7832 | nan             | 0.8233    | 0.9021 | 0.8609 | 0.9737   |
| 0.0037        | 9.0   | 8811 | nan             | 0.8281    | 0.9010 | 0.8630 | 0.9712   |
| 0.0031        | 10.0  | 9790 | nan             | 0.8295    | 0.8966 | 0.8617 | 0.9714   |

### Testing Results
metrics={'test_loss': 0.07461995631456375, 'test_precision': 0.8852040816326531, 'test_recall': 0.9137590520079, 'test_f1': 0.8992549400712667, 'test_accuracy': 0.9883402014967543, 'test_runtime': 13.0766, 'test_samples_per_second': 106.297, 'test_steps_per_second': 13.306})

### Framework versions

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