---
base_model: daveni/twitter-xlm-roberta-emotion-es
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: base
  results: []
pipeline_tag: text-classification
---

<!-- 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: 0.4881
- Accuracy: 0.8504
- F1: 0.8119
- Precision: 0.8454
- Recall: 0.7810

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.667         | 1.0   | 32   | 0.5528          | 0.7283   | 0.6057 | 0.7571    | 0.5048 |
| 0.5241        | 2.0   | 64   | 0.4843          | 0.7874   | 0.7065 | 0.8228    | 0.6190 |
| 0.3046        | 3.0   | 96   | 0.4785          | 0.8031   | 0.7423 | 0.8090    | 0.6857 |
| 0.1631        | 4.0   | 128  | 0.4776          | 0.8228   | 0.7644 | 0.8488    | 0.6952 |
| 0.097         | 5.0   | 160  | 0.4881          | 0.8504   | 0.8119 | 0.8454    | 0.7810 |


### Framework versions

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