---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ptbr-finetuned
  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. -->

# xlm-roberta-base-ptbr-finetuned

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3305
- F1: 0.2957
- Roc Auc: 0.6147
- Accuracy: 0.48

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.3954        | 1.0   | 109  | 0.3836          | 0.0    | 0.5     | 0.23     |
| 0.3868        | 2.0   | 218  | 0.3428          | 0.1905 | 0.5658  | 0.415    |
| 0.3374        | 3.0   | 327  | 0.3169          | 0.2236 | 0.5866  | 0.48     |
| 0.2931        | 4.0   | 436  | 0.3217          | 0.2091 | 0.5789  | 0.425    |
| 0.2601        | 5.0   | 545  | 0.3166          | 0.2752 | 0.6008  | 0.485    |
| 0.2381        | 6.0   | 654  | 0.3305          | 0.2957 | 0.6147  | 0.48     |
| 0.1987        | 7.0   | 763  | 0.3561          | 0.2761 | 0.6045  | 0.455    |
| 0.1863        | 8.0   | 872  | 0.3538          | 0.2905 | 0.6121  | 0.465    |
| 0.1335        | 9.0   | 981  | 0.3741          | 0.2710 | 0.6029  | 0.445    |
| 0.1343        | 10.0  | 1090 | 0.3873          | 0.2850 | 0.6096  | 0.46     |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0