---
base_model: daveni/twitter-xlm-roberta-emotion-es
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: base
  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. -->

# base

This model is a fine-tuned version of [daveni/twitter-xlm-roberta-emotion-es](https://huggingface.co./daveni/twitter-xlm-roberta-emotion-es) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1501
- Accuracy: 0.8346
- F1: 0.7717
- Precision: 0.8987
- Recall: 0.6762

## 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: 5e-05
- train_batch_size: 32
- 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     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6694        | 1.0   | 32   | 0.6008          | 0.6969   | 0.4901 | 0.8043    | 0.3524 |
| 0.5305        | 2.0   | 64   | 0.4786          | 0.7795   | 0.6854 | 0.8356    | 0.5810 |
| 0.3457        | 3.0   | 96   | 0.4456          | 0.8031   | 0.7788 | 0.7273    | 0.8381 |
| 0.2513        | 4.0   | 128  | 0.5167          | 0.8307   | 0.7725 | 0.8690    | 0.6952 |
| 0.1725        | 5.0   | 160  | 0.6917          | 0.8189   | 0.7604 | 0.8391    | 0.6952 |
| 0.0974        | 6.0   | 192  | 0.7955          | 0.8228   | 0.7619 | 0.8571    | 0.6857 |
| 0.042         | 7.0   | 224  | 0.8829          | 0.8346   | 0.7766 | 0.8795    | 0.6952 |
| 0.014         | 8.0   | 256  | 0.9991          | 0.8189   | 0.7653 | 0.8242    | 0.7143 |
| 0.0103        | 9.0   | 288  | 1.1313          | 0.8346   | 0.7717 | 0.8987    | 0.6762 |
| 0.0194        | 10.0  | 320  | 1.1501          | 0.8346   | 0.7717 | 0.8987    | 0.6762 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0
- Datasets 2.20.0
- Tokenizers 0.19.1