---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fedcsis_translated-intent_baseline-xlm_r-pl
  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. -->

# fedcsis_translated-intent_baseline-xlm_r-pl

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the
[leyzer-fedcsis-translated](https://huggingface.co/datasets/cartesinus/leyzer-fedcsis-translated) dataset.

Results on untranslated test set:
- Accuracy: 0.8769

It achieves the following results on the evaluation set:
- Loss: 0.5478
- Accuracy: 0.8769
- F1: 0.8769

## 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: 16
- eval_batch_size: 16
- 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 | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 3.505         | 1.0   | 814  | 1.8819          | 0.5979   | 0.5979 |
| 1.5056        | 2.0   | 1628 | 1.1033          | 0.7611   | 0.7611 |
| 1.0892        | 3.0   | 2442 | 0.7402          | 0.8470   | 0.8470 |
| 0.648         | 4.0   | 3256 | 0.5263          | 0.8902   | 0.8902 |
| 0.423         | 5.0   | 4070 | 0.4253          | 0.9152   | 0.9152 |
| 0.3429        | 6.0   | 4884 | 0.3654          | 0.9194   | 0.9194 |
| 0.2464        | 7.0   | 5698 | 0.3213          | 0.9273   | 0.9273 |
| 0.1873        | 8.0   | 6512 | 0.3065          | 0.9328   | 0.9328 |
| 0.1666        | 9.0   | 7326 | 0.3046          | 0.9345   | 0.9345 |
| 0.1459        | 10.0  | 8140 | 0.2911          | 0.9370   | 0.9370 |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2