---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fedcsis-slot_baseline-xlm_r-es
  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-slot_baseline-xlm_r-es

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: 0.0521
- Precision: 0.9728
- Recall: 0.9711
- F1: 0.9720
- Accuracy: 0.9914

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7183        | 1.0   | 941  | 0.1287          | 0.9389    | 0.9429 | 0.9409 | 0.9802   |
| 0.0792        | 2.0   | 1882 | 0.0698          | 0.9551    | 0.9609 | 0.9580 | 0.9876   |
| 0.0502        | 3.0   | 2823 | 0.0586          | 0.9623    | 0.9624 | 0.9624 | 0.9886   |
| 0.0312        | 4.0   | 3764 | 0.0511          | 0.9697    | 0.9668 | 0.9682 | 0.9904   |
| 0.0229        | 5.0   | 4705 | 0.0494          | 0.9715    | 0.9687 | 0.9701 | 0.9913   |
| 0.021         | 6.0   | 5646 | 0.0447          | 0.9697    | 0.9680 | 0.9689 | 0.9911   |
| 0.0139        | 7.0   | 6587 | 0.0512          | 0.9715    | 0.9691 | 0.9703 | 0.9915   |
| 0.0126        | 8.0   | 7528 | 0.0507          | 0.9713    | 0.9699 | 0.9706 | 0.9913   |
| 0.01          | 9.0   | 8469 | 0.0500          | 0.9720    | 0.9702 | 0.9711 | 0.9913   |
| 0.0072        | 10.0  | 9410 | 0.0521          | 0.9728    | 0.9711 | 0.9720 | 0.9914   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2