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---
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
|