---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- told-br
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-finetuned-told-br
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: told-br
      type: told-br
      config: binary
      split: validation
      args: binary
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7457142857142857
    - name: F1
      type: f1
      value: 0.7452494655157376
---

<!-- 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-finetuned-told-br

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co./xlm-roberta-base) on the told-br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4925
- Accuracy: 0.7457
- F1: 0.7452

## 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: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5935        | 1.0   | 263  | 0.4970          | 0.7338   | 0.7350 |
| 0.4797        | 2.0   | 526  | 0.4925          | 0.7457   | 0.7452 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1